If you want something included or changed, please email me.. ara@sics.se
The tutorials are not structured in any particular way yet.. "Find"
in the browser will have to do.
Perhaps i'll experiment with sturcutring and indexing this and the
texts later.
Check out MITECS:
http://mitpress.mit.edu/MITECS/
or AUAI Tutorial and Survey Sources
http://auai.org/auai-tutes.html
or USENET FAQs:
ftp://rtfm.mit.edu/pub/usenet/
or AltaVista's "Refernce & Education" section
http://altavista.looksmart.com/eus1/eus53706/r?l&izf&
or "Entropy on the World Wide Web"
http://www.math.washington.edu/~hillman/entropy.html
or "THE NET ADVANCE OF PHYSICS"
Review Articles and
Tutorials in an Encyclopædic Format
http://web.mit.edu/afs/athena.mit.edu/user/r/e/redingtn/www/netadv/welcome.html
or The Universal Library (online-books and articles)
http://www.ulib.org/webRoot/Books/
or AI Topics. AAAI's lists some authoritative, non-technical resources organized and annotated to provide access to basic information about AI. http://www.aaai.org/Pathfinder/pathfinder.html
Online Books in Science and Mathematics
http://digital.library.upenn.edu/webbin/book/subjectstart?QA
e.g "Numerical Recipes in C" book On-Line
http://www.ulib.org/webRoot/Books/Numerical_Recipes/bookcpdf.html
World Lecture Hall.
The World Lecture Hall (WLH) contains links to pages created by faculty
worldwide who are using
the Web to deliver class materials.
http://www.utexas.edu/world/lecture/
Rakesh Dugad and U. B. Desai
A TUTORIAL ON HIDDEN MARKOV MODELS
Tutorial, Indian Institute of TechBombaynology -, Number SPANN-96-1,
May 1996.
http://uirvli.ai.uiuc.edu/dugad/guestbook/addHMMguest.html
http://uirvli.ai.uiuc.edu/dugad/hmm_tut.htm
David Heckerman
A Tutorial on Learning Bayesian Networks
Technical Report, Microsoft Research, Number MSR-TR-95-06, March 1995.
ftp://research.microsoft.com/pub/Tech-Reports/Winter94-95/TR-95-06.PS
The Rete Algorithm
ingargiola@cis.temple.edu
http://yoda.cis.temple.edu:8080/UGAIWWW/lectures/rete.html
PAC Learning
ingargiola@cis.temple.edu
http://yoda.cis.temple.edu:8080/UGAIWWW/lectures95/learn/pac/pac.html
The Loom Tutorial
For Loom Release 2.1
http://www.isi.edu/isd/LOOM/documentation/tutorial2.1.html
Least Square Fit, Hae Chang Gea
http://www.rci.rutgers.edu/~gea/notes/least.html
A Conceptual Framework for Text Filtering
http://www.clis.umd.edu/dlrg/filter/papers/filter.ps
A technical report presenting a selective survey of present practice
in
information filtering with an emphasis on defining the field and
identifying significant research issues. A significantly improved
version will appear in the journal User Modeling and User-Adapted
Interaction in 1997.
Survey of IR and IF
http://www.enee.umd.edu/medlab/filter/papers/survey.ps
Multiagent Systems: A Survey from a Machine Learning Perspective
http://www.cs.cmu.edu/afs/cs/usr/pstone/public/papers/96ieee-survey/survey.html
A SURVEY ON INTELLIGENT AGENTS IN TELECOMMUNICATIONS
http://www.cs.tcd.ie/research_groups/aig/iag/survey.html
Avrim Blum's recent survey talks and survey papers
http://www.cs.cmu.edu/~avrim/surveys.html
Including: "On-line Algorithms in Machine Learning", "On-line
Exploration and Navigation" and "A Tutorial on Computational Learning
Theory"
Reinforcement Learning: A survey, Leslie Pack Knaelbing, Michael L.
Littman, Andrew W. Moore
http://www.cs.brown.edu/people/lpk/rl-survey.ps
An Introduction To Artificial Life
http://lslwww.epfl.ch/~moshes/introal/introal.html
Explorations in Artificial Life (special issue of AI Expert),
pages 4-8, September, 1995. Miller Freeman.
Case-Based Reasoning: Foundational Issues, Methodological Variations,
and System Approaches
http://www.iiia.csic.es/People/enric/AICom.html
AICom - Artificial Intelligence Communications, Vol. 7, No. 1. 1996
Maskininlärning och adaption (Swedish)
http://www.d.kth.se/~d93-tol/ml/uppsats/
En uppsats om inlärning och adaption.
Defining Data Mining
http://www.dbmsmag.com/9608d53.html
Data mining is one of the hottest topics in information technology.
This
article is an introduction to data mining: what it is, why it's
important, and how it can be used to provide increased understanding
of
critical relationships in rapidly expanding
corporate data warehouses.
From artificial individuals to global patterns.
ftp://alife.santafe.edu/pub/PAPERS/ARCHIVE/faitgp.ps.gz
Fuzzy Systems :Tutorials/Lectures
http://www.it.uom.gr/pdp/DigitalLib/Fuzzy/fuzzy_lect.htm
Artificial Neural Networks Technology, an 83 page report, by DACS
http://www.dacs.dtic.mil/techs/neural/neural_ToC.html
This report describes what artificial neural networks are, how to use
them, and where they are currently being applied. A brief
overview of neural networks and their history is provided.
Neural Networks, By Michael I. Jordan, Christopher M. Bishop,
26 pages
An overview of current research on artificial neural networks, emphasizing
a statistical perspective.
ftp://publications.ai.mit.edu/ai-publications/1500-1999/AIM-1562.ps.Z
Some resources about case-based reasoning. the "AU-CBR Classroom".
http://www.ai-cbr.org/classroom/classroom.html
Case-Based Reasoning: A Review . Ian Watson & Farhi Marir
http://www.ai-cbr.org/classroom/cbr-review.html
CBR in Context: The Present and Future. David B. Leake. In Leake, D.,
editor, Case-Based Reasoning: Experiences, Lessons, and Future
Directions, AAAI Press/MIT Press, 1996. 35 pages.
http://www.cs.indiana.edu/~leake/papers/a-96-01.html
"Is this document relevant?...probably": a survey of probabilistic models
in information retrieval
Fabio Crestani ,Mounia Lalmas , Cornelis J. Van Rijsbergen ,Iain Campbell
http://www.acm.org/pubs/articles/journals/surveys/1998-30-4/p528-crestani/p528-crestani.pdf
Artificial Life, Thomas S. Ray
http://www.hip.atr.co.jp/~ray/pubs/fatm/fatm.html
DNA Computing: the arrival of biological mathematics. Lila Kari
http://www.csd.uwo.ca/~lila/intel.ps
Fuzzy Logic Primer - A Brief Intoduction to Fuzzy Logic, Togai InfraLogic
Inc., 1992,
(http://www-cgi.cs.cmu.edu/afs/cs/project/ai-repository/ai/areas/fuzzy/doc/intro/primer.tgz)
Gary T. Leavens
Introduction to the Literature on Semantics
Technical Report, Iowa State University, Department
of Computer Science, Number 94-15, August
1994.
ftp://ftp.cs.iastate.edu/pub/techreports/TR94-15/TR.ps.Z
Mary Campione and Kathy Walrath
The Java Tutorial Second Edition: Object-Oriented
Programming for the Internet
, The Java Series, Addison-Wesley, 1998.
http://java.sun.com/docs/books/tutorial/2e/book.html
http://java.sun.com/docs/books/tutorial/index.html
Jim Woodcock and Jim Davies
Using Z: Specification, Refinement, and Proof
, Prentice Hall International Series in Computer
Science, 1996.
http://www.comlab.ox.ac.uk/usingz.html
Zena M. Ariola and Jan Willem Klop and Detlef Plump
Bisimilarity in term graph rewriting
161, p. 22, Centrum voor Wiskunde en Informatica
(CWI), January 31 1998.
ftp://ftp.cwi.nl/pub/CWIreports/SEN/SEN-R9801.ps.Z
Yachin Pnueli
Digital Image Compression -- A Brief Overview
Technical Report, Institut für Informatik,
Freie Universität Berlin, Germany, Number B 94-14, June 1994
http://visinfo.zib.de/EVlib/Show?EVL-1994-101
Ronald C. Arkin
Intelligent Robotic Systems - Editorial Introduction
Technical Report.
ftp://ftp.cc.gatech.edu/pub/people/arkin/web-papers/expert-intro.ps.Z
R. R. MUNTZ and John C. S. LUI
An Eclectic Survey of Bounding Methods for Markov
Chain Models
Technical Report, Chinese University of Hong Kong,
Number CS-TR-94-08, 1995.
ftp://ftp.cs.cuhk.hk/pub/techreports/./94/tr-94-8.ps.gz
Henry Lieberman
Intelligent Interface Agents
Proceedings of the 1998 International Conference
on Intelligent User Interfaces, Tutorial, p. 3, 1998
http://www.acm.org/pubs/articles/proceedings/uist/268389/p3-lieberman/p3-lieberman.pdf
H.-P. Seidel
An introduction to polar forms
IEEE, Vol. 1, pp. 38-46, 1993.
http://visinfo.zib.de/EVlib/Show?EVL-1993-10
Matthew S. Ryan and Graham R. Nudd
The Viterbi Algorithm
Research Report, Department of Computer Science,
University of Warwick, Number CS-RR-238,
February 1993.
http://www.dcs.warwick.ac.uk/pub/reports/rr/238.html
N. R. Scaife
A Survey of Concurrent Object-Oriented Programming
Languages
Research Memo, Heriot-Watt University, Number RM/96/4,
February 1996.
ftp://ftp.cee.hw.ac.uk/pub/funcprog/nrs.coop96.ps.Z
M Tofte
Four Lectures on Standard ML
Technical Report, Laboratory for Foundations of
Computer Science. Department of Computer Science,
Edinburgh University, Number ECS LFCS 89 73, March
89.
ftp://ftp.diku.dk/pub/diku/semantics/papers/D-112.part1.dvi.Z
S. Kahrs and D. Sannella and A. Tarlecki
The semantics of Extended ML: A gentle introduction
Proc. Intl. Workshop on Semantics of Specification
Languages, Springer Workshops in Computing, 93.
http://www.dcs.ed.ac.uk/staff/dts/pub/gentle.ps
Jan Willem Klop and Vincent van Oostrom and Femke
van Raamsdon
Combinatory Reduction Systems: introduction and
survey
Technical Report, Vrije Universiteit Amsterdam,
Computer Science, Number IR-327, June 1993.
ftp://ftp.cs.vu.nl/pub/papers/theory/IR-327.ps.Z
Jonathan M. D. Hill and Keith Clarke
An introduction to category theory, category theory
monads, and their relationship to functional
programming
Technical Report, Department of Computer Science,
Queen Mary & Westfield College, Number
QMW-DCS-681, August 1994.
http://www.dcs.qmw.ac.uk/publications/reports/qmw681/qmw681.htmlftp://ftp.dcs.qmw.ac.uk/cpc/jon_hill/qmw681.ps.Z
Paolo A. G. Sivilotti and Peter A. Carlin
A Tutorial for CC++
Technical Report, California Institute of Technology,
Number CS-TR-94-02, 1994.
http://www.cs.caltech.edu/comp/CCplusplus.html
Florian Matthes and Stephan Ziemer
Understanding SAP R/3:A Tutorial for Computer Scientists
Technical Report, Arbeitsbereich Softwaresysteme,
Technische Universität Hamburg-Harburg, Germany,
March 1998.
http://www.sts.tu-harburg.de/papers/1998/MaZi98
John R. Koza
Genetic Programming: Automatic Programming of Computers
EvoNews, 1(3), pp. 4-7, March 1997.
http://www.dcs.napier.ac.uk/evonet/evonews.htm
Mark Willis and Hugo Hiden and Peter Marenbach and
Ben McKay
Genetic Programming: An Introduction and Survey
of Applications
Second International Conference on Genetic ALgorithms
in Engineering Systems: Innovations and
Applications GALESIA",, Institution of Electrical
Engineers, 1-4 September 1997. http://lorien.ncl.ac.uk/sorg/paper14.ps
J. Kohlas and P. A. Monney
Theory of Evidence - a Survey of its Mathematical
Foundations, Applications and
Computational Anaylsis
ZOR- Mathematical Methods of Operations Research,
Vol. 39, pp. 35-68, 1994.
http://www2-iiuf.unifr.ch/tcs/publications/ps/abhkl98.ps.gz
Neil Immerman
Descriptive and Computational Complexity
Lecture Notes AMS Short Course in Computational
Complexity Theory, Atlanta, GA, 5--6 Jan 1988,
Proceedings of Symposia in Applied Mathematics,
Vol. 38, pp. 75-91, AMS, 1989.
http://www.cs.umass.edu/~immerman/ams-survey.ps
C. Barry Jay
An Introduction to Categories in Computing
Technical report, School of Computing Sciences,
Univ. of Techn. Sidney",, Number UTS-SOCS-93.9,
1993.
ftp://ftp.socs.uts.edu.au/Users/cbj/catnotes.ps.Z
Bart Jacobs and Jan Rutten
A Tutorial on (Co)Algebras and (Co)Induction
Bulletin of the EATCS, Vol. 62, pp. 222-259, 1996.
http://www.cs.kun.nl/~bart/PAPERS/JR.ps.Z
H. F. Hofmann
Requirements Engineering: A Survey of Methods and
Tools
Technical Report, Institut für Informatik der
Universität Zurich, Number 93.05, 1993
ftp://ftp.ifi.unizh.ch/pub/techreports
Ian Utting
Postscript Tutorial and Reference
Technical Report, University of Kent, Computing
Laboratory, Number 11-92*, June 1992.
http://www.cs.ukc.ac.uk/pubs/1992/109
Zahir Ebrahim
A Brief Tutorial on ATM (DRAFT)
, March 1992.
ftp://datanet.tele.fi/atm/articles/atm-intro.txt
Guido van Rossum
Python Tutorial
Technical Report, CWI, October 1995
http://www.python.org/doc/tut/tut.html
David Chaum
Prepaid Smart Card Techiques: A Brief Introduction
and Comparison
, 1994.
http://www.digicash.com/publish/cardcom.html
Andre Berthiaume
Complexity Theory Retrospective II , SV, 1996.
"good but non-trivial introduction to quantum cryptography"
http://www.cwi.nl/%7eberthiau/publications/CTR.ps.gz
S. Homer and A. Selman
Complexity Theory
Technical Report, Department of Computer Science,
SUNY Buffalo, Number 91-04, June 8 1992.
ftp://ftp.cs.buffalo.edu/pub/tech-reports/91-04.ps.Z
Andrew D. Birrell
An Introduction to Programming with Threads.
Technical Report, Digital Equipment Corporation,
Systems Research Centre, Number 35, p. 35
pages., 6 January 1989.
ftp://ftp.digital.com/pub/DEC/SRC/research-reports/SRC-035.ps.Z
S. H. G. ten Hagen and B. J. A. Kröse
A Short Introduction to Reinforcement Learning
Proc. of the 7th Belgian-Dutch Conf. on Machine
Learning, pp. 7-12, October 1997.
ftp://ftp.wins.uva.nl/pub/computer-systems/aut-sys/reports/HagKro97b.ps.gz
B. J. A. Kröse and P. P. van der Smagt
An Introduction to Neural Networks
, University of Amsterdam, 1994.
ftp://ftp.wins.uva.nl/pub/computer-systems/aut-sys/reports/neuro-intro/neuro-intro.ps.gz
Stewart M. Clamen
Data Persistence in Programming Languages -- A Survey
Technical Report, Carnegie Mellon University, Number
CMU-CS-91-155, May 1991.
ftp://reports.adm.cs.cmu.edu/1991/CMU-CS-91-155.ps
Marcelo P. Fiore and Achim Jung and Eugenio Moggi and Peter O'Hearn
and Jon Riecke and Giuseppe
Rosolini and Ian Stark
Domains and Denotational Semantics: History Accomplishments
and Open Problems",
Technical Report, University of Birmingham, School
of Computer Science, Number CSR-96-2, January
1996.
ftp://ftp.cs.bham.ac.uk/pub/tech-reports/1996/CSR-96-02.ps.gz
Nadia Pisanti
A survey on DNA computing
Technical Report, Univerità di Pisa, Number
TR-97-07, April 1997. http://lite.ncstrl.org:3803/Dienst/UI/2.0/Describe/ncstrl.unipi_it%2fTR-97-07
Bill Rieken
An Introduction to Java Beans
, 1997.
http://dxsting.cern.ch/sting/COOTS97.html
L. P. Kaelbling and M. L. Littman and A. W. Moore
Reinforcement Learning: A Survey
Technical Report, Number 9605103, May 1, 1996
http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a.pdf
http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a.ps
http://www.cs.cmu.edu/afs/cs/project/jair/pub/volume4/kaelbling96a-html/rl-survey.html
Eric Miller
An Introduction to the Resource Description Framework
D-Lib Magazine, May 1998.
http://www.dlib.org/dlib/may98/miller/05miller.html
Mark Wooldridge and Nick Jennings
Agent Theories, Architectures, and Languages: A
Survey
, Vol. 890, pp. 1-32, 1994.
ftp://ftp.elec.qmw.ac.uk/pub/isag/distributed-ai/publications/ECAI94-WS.ps.Z
Jørgen Bang-Jensen and Gregory Gutin
Generalizations of tournaments: A survey
Technical Report, Department of Mathematics and
Computer Science, Odense University, Number
PP-1996-25, October 1 1996.
ftp://ftp.imada.ou.dk/pub/papers/pp-1996/25.ps.gz
Brani Vidakovi\'c and Peter Müller
Wavelets for kids: A tutorial introduction
, 1994.
ftp://ftp.isds.duke.edu/pub/brani/papers/wav4kids%5bA-B%5d.ps.Z
Burt Kaliski
Introduction to Public-Key Technology
Technical Report, 1993 RSA Data Security Conference,
1993. http://julmara.ce.chalmers.se/Security/intro.ps.gz
Peter D. Mosses
A Tutorial on Action Semantics
, March 1996
ftp://ftp.brics.dk/pub/BRICS/Projects/AS/Papers/Mosses96DRAFT/
B. J. A. Kr$\ddot\mboxo$se and P. P. Van der Smagt
An Introduction to Neural Networks
, 1993
. http://www.fwi.uva.nl/research/vg4/neuro/courses/neural.html
Mika Hirvensalo
Quantum Error Correction
Technical Report, TUCS - Turku Centre for Computer
Science, Number TUCS-TR-178, May 19 1998.
http://www.tucs.fi/publications/techreports/TR178.html
Listed by first author.
Machine Learning Research: Four Current Directions by Tom Dietterich.
ftp://ftp.cs.orst.edu/pub/tgd/papers/aimag-survey.ps.gz
AIM - Steve Belleguelle
An online course on AI.
http://tawny.cs.nott.ac.uk/~sbx/winnie/aim/overview.htm
Game Theory: An Introductory Sketch - Roger A. McCain
http://william-king.www.drexel.edu/top/eco/game/game.html
Economic and Game Theory - David Levine
http://levine.sscnet.ucla.edu/index.htm
The site includes getting-started information and a good collection
of links, as well as specialist resources. The emphasis on
economics is moderate.
MLCOURSE teachpack - Chris Thornton
Formerly published as web pages, this is now available only as postscript.
Machine learning for behavior acquisition by a
mobile robot. Much of the content relies on the robot only for motivation.
http://www.cogs.susx.ac.uk/users/christ/teachpacks/mlcourse/text.ps
An overview of genetic algorithms: Part 1, fundamentals (1993,
16 pages)
David Beasley, David R. Bull, Ralph R. Martin
ftp://ftp.cs.wayne.edu/pub/EC/GA/papers/over93.ps.gz
This compressed postscript paper goes into more detail on how and why
GA's work, but doesn't have as many references.
Neural Nets Course - Kevin Gurney
http://www.shef.ac.uk/psychology/gurney/notes/index.html
Lecture notes, homework, software, and other information for a graduate-level
neural networks course. It looks
self-contained; you should be able to work through the course on your
own.
Backpropagator's Review - Don Tveter
http://www.dontveter.com/bpr/bpr.html
A broad review of backpropagation neural networks, with explanations,
advice, and information about many variants on the
basic algorithm.
An introduction to neural networks, Ben Kröse and Patrick van der
Smagt
http://carol.wins.uva.nl/~krose/col/nn/neuro-intro.html
N. Immerman, Descriptive and Computational Complexity, in Computational
Complexity Theory, ed. J. Hartmanis, Lecture Notes for AMS Short
Course on ComputationComplexity Theory, Proc. Symp. in Applied Math. 38,
American Mathematical Society (1989), 75-91.
http://www.cs.umass.edu/~immerman/pub/survey.ps
Lecture Notes: Concise Introduction to AI, 1991, Stefan
Arnborg
ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/ai2d1963.ps.Z
Automata and BDDs - new tools in verification and optimization, Stefan
Arnborg, 1994
http://www.nada.kth.se/~stefan/bdds.dvi
Theory of decomposable structures, 1992, Stefan Arnborg,
ftp://ftp.nada.kth.se/pub/documents/Theory/Stefan-Arnborg/gdnotes.ps
Introduction to Minimum Encoding Inference ( by Jonathan J. Oliver and
David Hand.
Abstract: This paper examines the minimum encoding approaches to inference,
Minimum Message Length (MML) and Minimum Description Length (MDL). This
paper was written with the objective of providing an introduction to this
area for statisticians. We describe coding techniques for data, and examine
how
these techniques can be applied to perform inference and model selection.
http://www.cs.monash.edu.au/~jono/Open_Uni/TR4-94.ps
Introduction to Radial Basis Function Networks by Mark J. L. Orr and
Buccleuch Place.
Abstract: This document is an introduction to radial basis function
(RBF) networks, a type of artificial neural network for application to
problems of supervised learning (e.g. regression, classification and time
series prediction). It is available in either PostScript or hyper-text
2. A package of Matlab routines 3 which implement the algorithms described
herein together with a PostScript user manual 4 are also available.
http://www.cns.ed.ac.uk/people/mark/papers/intro.ps
Adaptive Resonance Theory (ART): An Introduction by L.G. Heins, D.R.
Tauritz and May/June.
Abstract: The purpose of this paper is to provide an introduction
to Adaptive Resonance Theory (ART) by examining ART-1, the first member
of the family of ART neural networks. The only prerequisite knowledge in
the area of neural networks necessary for understanding this paper is backpropagation
[Hinton86]. For an easy introduction to neural networks see [Freeman91],
for a more in depth overview of the field see [Hertz91]. http://www.wi.leidenuniv.nl/art/paper.ps
MDL and MML : Similarities and Differences (Introduction to Minimum
Encoding Inference | Part III) by Rohan A. Baxter and Jonathan J. Oliver.
Abstract: This paper continues the introduction to minimum encoding
inductive inference given by Oliver and Hand. This series of papers was
written with the objective of providing an introduction to this area for
statisticians. We describe the message length estimates used in Wallace's
Minimum Message Length
(MML) inference and Rissanen's Minimum Description Length (MDL) inference.
The dierences in the message length estimates of the two approaches are
explained. The implications of these dierences for applications are discussed.
http://www.cs.monash.edu.au/~rohan/PAPERS/intro.3.ps
Evaluation and Selection of Biases in Machine Learning by Diana F. Gordon,
MARIE desJARDINS and Thomas G. Dietterich.
Abstract: In this introduction, we define the term bias as it is used
in machine learning systems. We motivate the importance of automated methods
for evaluating and selecting biases using a framework of bias selection
as search in bias and meta-bias spaces. Recent research in the field of
machine learning bias is summarized.
http://www.aic.nrl.navy.mil/~gordon/papers/mlj95.ps
Pac Learning, Noise, and Geometry by Robert H. Sloan and S. Morgan.
Abstract: This paper describes the probably approximately correct model
of concept learning, paying special attention to the case where instances
are points in Euclidean n-space. The problem of learning from noisy training
data is also studied.
http://www.eecs.uic.edu/~sloan/birk-circulate.ps
Technology Overview: A Report on Data Mining by Cscs Tr and May.
Abstract: In this report we present a technology assessment of a spectrum
of mathematical methods and software techniques for data mining, i.e.,
for nding patterns and regularities in sets of data. After an introduction
to the subject, the second part of the paper describes the essentials of
a variety of methods for data mining through supervised and un-supervised
learning. In the third part of the report we present results from the application
of data mining techniques to dierent application areas such as chemical
and pharmaceutical chemistry, retail and consumer banking, remote sensing,
and the handling of engineering and maintenance data.
ftp://ftp.cscs.ch/pub/CSCS/techreports/1995/CSCS-TR-95-02.ps.gz
MML and Bayesianism: Similarities and Differences ( by Jonathan
J. Oliver and Rohan A. Baxter.
Abstract: This paper continues the introduction to minimum encoding
inference given by Oliver and Hand. This series of papers were written
with the objective of providing an introduction to this area for statisticians.
We examine the relationship between Bayesianism and Minimum Message Length
(MML) inference. We argue that MML augments Bayesian methods by providing
a sound Bayesian method for point estimation which is invariant under non-linear
transformations. We explore the issues of invariance of estimators under
non-linear transformations, the role of the Fisher Information matrix in
MML inference, and the apparent similarity between MML and the adoption
of a Jeffreys' Prior. We then compare MML to an approximate method of Bayesian
Model Class Selection. Despite apparent similarities in their expressions,
the properties of the two approaches can be different. http://www.cs.monash.edu.au/~rohan/PAPERS/intro.2.ps
An Introduction to Program Specialisation by Type Inference by John
Hughes, Phil Wadler and Neil Jones.
Abstract: Capsule review by Phil Wadler About a dozen years ago, Neil
Jones and colleagues set the world on fire by demonstrating the first self-applicable
partial evaluator. A little later, Jones formally listed key open problems
in the area, one of which was to build an optimal specialiser for a typed
language, and
that chestnut remained uncracked a decade later. Here John Hughes shows
how a change of perspective enabled him to break it open, and split a few
other tough nuts as well. This paper complements the full work published
elsewhere, by conveying the insights and intuitions that can be obscured
when all the i's must be
dotted and all the t's crossed. The full work is destined to become
a classic; this is its essential companion.
http://ftp.dcs.glasgow.ac.uk/fp/workshops/fpw96/Hughes.ps.gz
Statistical Techniques for Language Recognition: An Introduction
and Guide for Cryptanalysts by Ravi Ganesan and Alan T.
Sherman.
Abstract: We explain how to apply statistical techniques to solve several
language-recognition problems that arise in cryptanalysis and other domains.
Language recognition is important in cryptanalysis because, among other
applications, an exhaustive key search of any cryptosystem from ciphertext
alone
requires a test that recognizes valid plaintext. Written for cryptanalysts,
this guide should also be helpful to others as an introduction to statistical
inference on Markov chains.
http://www.cs.umbc.edu/pub/REPORTS/cs-93-02.ps
Multiagent Systems: A Survey from a Machine Learning Perspective by
P. Stone and M. Veloso.
Abstract: Distributed Artificial Intelligence (DAI) has existed as
a subfield of AI for less than two decades. DAI is concerned with systems
that consist of multiple independent entities that interact in a domain.
Traditionally, DAI has focussed on the information management aspects of
these systems. But in the past few years, a subfield of DAI focussing on
behavior management, as opposed to information management, has emerged.
This young subfield is called Multiagent Systems (MAS). This survey of
MAS is intended to serve as an introduction to the field and as an organizational
framework. It contains guidelines for when and how MAS should be used to
build complex systems. A series of increasingly complex general multiagent
scenarios are presented. For each scenario, the issues that arise are described
along with a sampling of the techniques that exist to deal with them. The
presented techniques are not exhaustive, but they highlight how multiagent
systems can be and have been used to build complex systems. When options
exist, the techniques presented are biased towards Machine Learning approaches.
Additional opportunities for applying Machine Learning to MAS are highlighted
and robotic soccer is presented as an appropriate testbed for MAS.
http://mobile.csie.ntu.edu.tw/~yjhsu/courses/u1760/papers/multiagent-survey.ps.gz
On The State of Evolutionary Computation by Kenneth De Jong and William
Spears.
Abstract: In the past few years the evolutionary computation landscape
has been rapidly changing as a result of increased levels of interaction
between various research groups and the injection of new ideas which challenge
old tenets. The effect has been simultaneously exciting, invigorating,
annoying, and bewildering to the old-timers as well as the new-comers to
the field. Emerging out of all of this activity are the beginnings of some
structure, some common themes, and some agreement on important open issues.
We attempt to summarize these emergent properties in this paper.
http://www.aic.nrl.navy.mil/~spears/papers/icga93.ps
Probabilistic Proof Systems (A Survey) by Oded Goldreich.
Abstract: Various types of probabilistic proof systems have played
a central role in the development of computer science in the last decade.
In this exposition, we concentrate on three such proof systems -- interactive
proofs, zero-knowledge proofs, and probabilistic checkable proofs -- stressing
the essential role of randomness in each of them.
ftp://ftp.eccc.uni-trier.de/pub/eccc/lectures/goldreich/pps1.ps
Advances in Network Information Discovery and Retrieval * by David Eichmann.
Abstract: Access to information using the Internet has undergone dramatic
change and expansion recently. The unrivaled success of the World Wide
Web has altered the Internet from something approachable only by the initiated
to something of a media craze - the information superhighway made manifest
in the personal `home page.' This paper surveys the beginnings of network
information discovery and retrieval, how the Web has created a surprising
level of integration of these systems, and where the current state of the
art lies in creating globally accessible information spaces and supporting
access to those information spaces.
http://mobile.csie.ntu.edu.tw/~yjhsu/courses/u1760/papers/eichmann.ps
Brooks, Subsumption, and Mobile Robots by Jerry Franke and John R. Surdu.
Abstract: A summary of subsumption architecture is presented. In this
paper we discuss the basic assumptions underlying Dr. Brooks' original
research into this architecture. We also discuss the physical grounding
hypothesis versus the symbol system hypothesis and the underlying concepts
behind subsumption. We describe how the subsumption architecture is implemented
and give some examples to illustrate these ideas. We then give some examples
of subsumption control systems in mobile robots. Finally, we discuss some
ongoing research into this area.
http://www.cs.fsu.edu/courses/ROBOTS/robots.ps
Reasoning About Knowledge: A Survey by Joseph Y. Halpern.
Abstract: In this survey, I attempt to identify and describe some of
the common threads that tie together work in reasoning about knowledge
in such diverse elds as philosophy, economics, linguistics, articial intelligence,
and theoretical computer science, with particular emphasis on work of the
past ve years, particularly in computer science.
http://mobile.csie.ntu.edu.tw/~yjhsu/courses/u1760/papers/knowledge_survey.ps
A Survey of Information Retrieval and Filtering Methods by Christos
Faloutsos and Douglas Oard.
Abstract: We survey the major techniques for information retrieval.
In the first part, we provide an overview of the traditional ones (full
text scanning, inversion, signature files and clustering). In the second
part we discuss attempts to include semantic information (natural language
processing, latent semantic indexing and neural networks).
http://www.enee.umd.edu/medlab/filter/papers/survey.ps
http://www.cs.jhu.edu/~weiss/oard.ps.gz
A Survey of Intron Research in Genetics by Annie S. Wu and Robert K.
Lindsay.
Abstract: A brief survey of biological research on non-coding DNA is
presented here. There has been growing interest in the effects of noncoding
segments in evolutionary algorithms (EAs). To better understand and conduct
research on non-coding segments and EAs, it is important to understand
the biological background of such work. This paper begins with a review
of basic genetics and terminology, describes the different types of non-coding
DNA, and then surveys recent intron research.
ftp://ftp.aic.nrl.navy.mil/pub/aswu/papers/ppsn.96.ps.gz
Simplifying Decision Trees: A Survey by Leonard A. Breslow and David
W. Aha.
Abstract: Induced decision trees are an extensively-researched solution
to classification tasks. For many practical tasks, the trees produced by
tree-generation algorithms are not comprehensible to users due to their
size and complexity. Although many tree induction algorithms have been
shown to produce simpler, more comprehensible trees (or data structures
derived from trees) with good classification accuracy, tree simplification
has usually been of secondary concern relative to accuracy and no attempt
has been made to survey the literature from the perspective of simplification.
We present a framework that organizes the approaches to tree simplification
and summarize and critique the approaches within this framework. The purpose
of this survey is to provide researchers and practitioners with a concise
overview of tree-simplification approaches and insight into their relative
capabilities. In our final discussion, we briefly describe some empirical
findings and discuss the application of tree induction algorithms to case
retrieval in case-based reasoning systems.
http://www.aic.nrl.navy.mil/papers/1996/AIC-96-014.ps.Z
Selection of Relevant Features and Examples in Machine Learning by Avrim
L. Blum and Pat Langley.
Abstract: In this survey, we review work in machine learning on methods
for handling data sets containing large amounts of irrelevant information.
We focus on two key issues: the problem of selecting relevant features,
and the problem of selecting relevant examples. We describe the advances
that have been made on these topics in both empirical and theoretical work
in machine learning, and we present a general framework that we use to
compare different methods. We close with some challenges for future work
in this area.
http://www.cs.cmu.edu/afs/cs.cmu.edu/user/avrim/www/Papers/relevance.ps.gz
Separate-and-Conquer Rule Learning by Johannes F.
Abstract: This paper is a survey of inductive rule learning algorithms
that use a separate-andconquer strategy. This strategy can be traced back
to the AQ learning system and still enjoys popularity as can be seen from
its frequent use in inductive logic programming systems. We will put this
wide variety of algorithms into a single framework and analyze them along
three dierent dimensions, namely their search, language and overtting avoidance
biases.
ftp://ftp.ai.univie.ac.at/papers/oefai-tr-96-25.ps.Z
Mental Models: A Survey by Karl B. Schwamb.
Abstract: This paper reviews a class of cognitive representations collectively
known as mental models. These representations purport to integrate certain
aspects of propositional and imagistic theories of knowledge representation
while enabling more detailed theories of human cognitive behavior. This
paper explores the nature of mental models by considering basic philosophical
issues including ontology and semantics. Psychological evidence supporting
the existence and identifying characteristics of these models are reviewed
in the areas of comprehension, problem solving, and learning. Where available,
computational
implementations of mental model theories are discussed. After a comparison
of mental models with more conventional knowledge representations, a brief
discussion and directions for future research are given.
http://www.isi.edu/soar/schwamb/papers/mm-survey.ps
On-Line Algorithms in Machine Learning by Avrim Blum.
Abstract: The areas of On-Line Algorithms and Machine Learning are
both concerned with problems of making decisions about the present based
only on knowledge of the past. Although these areas differ in terms of
their emphasis and the problems typically studied, there are a collection
of results in Computational Learning Theory that fit nicely into the "on-line
algorithms" framework. This survey article discusses some of the results,
models, and open problems from Computational Learning Theory that seem
particularly interesting from the point of view of on-line algorithms.
The emphasis in this article is on describing some of the simpler, more
intuitive results, whose proofs can be given in their entirity. Pointers
to the literature are given for more sophisticated versions of these algorithms.
http://www.cs.cmu.edu/afs/cs.cmu.edu/user/avrim/www/Papers/survey.ps.gz
Measures in Collection Ranking Evaluation by Zhihong Lu, James P. Callan
and W. Bruce Croft.
Abstract: As a technique to hunt information on the Internet, collection
location has received more attention. Several approaches have been proposed
to solve this problem. All these approaches adopt the same procedure: ranking
the collections and returning the top-ranks. But these approaches define
different measures to evaluate collection ranking and the measures have
significant weaknesses. In this paper, we survey the measures used in current
research and propose a new pair of measures that are based on the
concepts of precision and recall. The new measures overcome the problems
found in the current measures.
http://ciir.cs.umass.edu/info/psfiles/irpubs/ir89.ps.gz
Perspectives of Current Research about the Complexity of Learning on
Neural Nets by Wolfgang Maass.
Abstract: This paper discusses within the framework of computational
learning theory the current state of knowledge and some open problems in
three areas of research about learning on feedforward neural nets.
ftp://archive.cis.ohio-state.edu/pub/neuroprose/maass.perspectives.ps.Z
An Overview of Sequence Comparison Algorithms in Molecular Biology *
by Eugene W. Myers.
Abstract: Molecular biologists frequently compare biosequences to see
if any similarities can be found in the hope that what is true of one sequence
either physically or functionally is true of its analogue. Such comparisons
are made in a variety of ways, some via rigorous algorithms, others by
manual means, and
others by a combination of these two extremes. The topic of sequence
comparison now has a rich history dating back over two decades. In this
survey we review the now classic and most established technique: dynamic
programming. Then a number of interesting variations of this basic problem
are examined that are
specifically motivated by applications in molecular biology. Finally,
we close with a discussion of some of the most recent and future trends.
http://www.cs.jhu.edu/~salzberg/readings/myers91.ps
A Logical Approach to Reasoning About Uncertainty: a Tutorial by Joseph
Y. Halpern.
Abstract: I consider a logical framework for modeling uncertainty,
based on the use of possible worlds, that incorporates knowledge, probability,
and time. This turns out to be a powerful approach for modeling many problems
of interest. I show how it can be used to give insights into (among other
things) several
well-known puzzles
http://www.cs.cornell.edu/home/halpern/papers/iccs.ps
A Genetic Algorithm Tutorial by Darrell Whitley.
Abstract: This tutorial covers the canonical genetic algorithm as well
as more experimental forms of genetic algorithms, including parallel island
models and parallel cellular genetic algorithms. The tutorial also illustrates
genetic search by hyperplane sampling. The theoretical foundations of genetic
algorithms are
reviewed, include the schema theorem as well as recently developed
exact models of the canonical genetic algorithm.
http://www.cs.colostate.edu/~whitley/tutorial.ps
Tutorial on Computational Learning Theory by Avrim Blum.
http://www.cs.cmu.edu/afs/cs.cmu.edu/user/avrim/www/Talks/cald_survey.ps.gz
Draft A Brief Introduction to Neural Networks by Richard D. De Veaux
and Lyle H. Ungar.
Abstract: Artificial neural networks are being used with increasing
frequency for high dimensional problems of regression or classification.
This article provides a tutorial overview of neural networks, focusing
on back propagation networks as a method for approximating nonlinear multivariable
functions. We explain, from a statistician's vantage point, why neural
networks might be attractive and how they compare to other modern regression
techniques. KEYWORDS: nonparametric regression; function approximation;
backpropagation.
http://www.cis.upenn.edu/~ungar/papers/nnet-intro.ps
Interaction with the Boyer-Moore Theorem Prover: A Tutorial Study Using
the Arithmetic-Geometric Mean Theorem by Matt
Kaufmann (CLI and Paolo Pecchiari.
Abstract: There are many papers describing problems solved using the
Boyer-Moore theorem prover, as well as papers describing new tools and
functionalities added to it. Unfortunately, so far, there has been no tutorial
paper describing typical interactions that a user has with this system
when trying to solve a
nontrivial problem, including a discussion of issues that arise in
these situations. In this paper we aim to fill this gap by illustrating
how we have proved an interesting theorem with the Boyer-Moore theorem
prover: a formalization of the assertion that the arithmetic mean of a
sequence of natural numbers is greater
than or equal to their geometric mean. We hope that this report will
be of value not only for (non-expert) users of this system, who can learn
some approaches (and tricks) to use when proving theorems with it, but
also for implementors of automated deduction systems. Perhaps our main
point is that, at least in the case
of Nqthm, the user can interact with the system without knowing much
about how it works inside. This perspective suggests the development of
theorem provers that allow interaction that is user oriented and not system
developer oriented.
ftp://dirleton.csres.utexas.edu/pub/reports/100.ps
Solving Recurrences with Generating Functions: A Tutorial by Katherine
Ann.
Abstract: Generating functions are a powerful technique for solving
recurrences. Recurrences are important in the analysis of algorithms because
the running time of an algorithm can be estimated by solving the corresponding
recurrence. As a tutorial, we present a method for solving recurrences
with generating
functions. In addition, as an application to cryptography, we explain
how generating functions can be used to analyze linear shift registers.
We introduce probability generating functions through an application involving
a queuing network model.
http://www.cs.umbc.edu/pub/REPORTS/cs-93-05.ps
Buntine: ExpFam, 1 Ultimode Systems Computation with the Exponential
Family and Graphical Models by Wray Buntine.
Abstract: This tutorial plays two roles: to illustrate how graphical
models can be used to present models and algorithms for data analysis,
and to present the exponential family as a central concept for computational
data analysis.The exponential family is the most important family of probability
distributions. It includes the Gaussian, the binomial, the Poisson, and
others. I
http://www.ultimode.com/~wray/EriceExpfonts.ps.gz
Buntine: Priors, 1 Ultimode Systems Prior Probabilities by Wray Buntine
and Prior Probabilities.
Abstract: Prior probabilities are the center of most of the old controversies
surrounding Bayesian statistics. While the Bayesian/Classical distinctions
in statistics are becoming blurred, priors remain a problem, largely because
of a lack of good tutorial material and the unfortunate residue of previous
misunderstandings. Methods for developing and assessing priors are now
routinely used by experienced practitioners. This tutorial will review
some of issues, presenting a unified perspective that incorporates decision
theory and multi-agent reasoning
http://www.ultimode.com/wray/EricePrior4up.ps.gz
<, Draft of Forthcomming Book.
http://robotics.stanford.edu/people/nilsson/mlbook.html
introduction to fractal image compression, view the SIGGRAPH '92
Course notes on fractal image compression
(977K). The SIGGRAPH '92 Course notes without the figures are
also available (80K).
http://inls.ucsd.edu/y/Fractals/fractal_paper.ps.gz
http://inls.ucsd.edu/y/Fractals/fractal_paper_no_figs.ps.Z
Reinforcement Learning; A Tutorial
Mance E. Harmon
http://www-anw.cs.umass.edu/~mharmon/rltutorial/
Heitkoetter, Joerg and Beasley, David,
eds. (1994) "The Hitch-Hiker's Guide to Evolutionary Computation:
A list of Frequently Asked Questions (FAQ)", USENET : comp.ai.genetic.
ftp://rtfm.mit.edu/pub/usenet/news.answers/ai-faq/genetic/
Sites for the Calculus 3 student, Originally created by Tim Chartier.
A Collection of intros and tutorials in calculus.
http://amath-www.colorado.edu/appm/courses/2350/Web/calc3site.html
Finite Mathematics & Applied Calculus Resource Page
On-line Interactive Tutorials , On-line game theory simulator , On-line
Calculus Topics , Tutorial on Related Rates , On-Line Numerical Integration,
Probability Distribution Generator and Grapher for Bernoulli
Trials | Markov system in action , Introduction to the Derivative , game
theory summary ,
http://www.hofstra.edu/~matscw/realworld.html
May, 1992. Auction Algorithms for Network Flow Problems: A Tutorial
Introduction. D.P. Bertsekas
http://donald-duck.mit.edu/lids/pubs/2108.html
Neural Network FAQ - Links to Paper Archives
Computation Theory FAQ - Kolmogorov Complexity
Information Content and Compression Limits FAQ
FAQs about Bayesian methods for neural networks
Introduction to Monte Carlo methods
David J C MacKay
A review paper to appear in the proceedings of a 1996 Erice summer
school, ed. M.Jordan.
ftp://wol.ra.phy.cam.ac.uk/pub/mackay/secret/erice.ps.gz
Gaussian Processes, David J C MacKay
http://131.111.48.24/pub/mackay/gp.ps.gz
Title: Bayesian Methods for Intelligent Data Analysis
Authors: Marco Ramoni and Paola Sebastiani
http://kmi.open.ac.uk/kmi-abstracts/kmi-tr-67-abstract.html
An Introduction to Bayesian Networks, Kevin Patrick Murphy
http://http.cs.berkeley.edu/~murphyk/Bayes/bayes.html
The CRC Concise Encyclopedia of Mathematics, Eric Weisstein
http://mathworld.wolfram.com/
G.O. Roberts and J.S. Rosenthal, Markov chain Monte Carlo: Some
practical implications of theoretical results.
ftp://markov.utstat.toronto.edu/jeff/bruns.ps.Z
J.S. Rosenthal, Convergence rates of Markov chains.
ftp://markov.utstat.toronto.edu/jeff/eigen.ps.Z
Markov Chain Monte Carlo (MCMC), Stas Anatolyev
http://www.ssc.wisc.edu/~sanatoly/mcmc.htm
Probabilistic Inference using Markov Chain Monte Carlo Methods,
Radford M. Neal
http://www.cs.utoronto.ca/~radford/review.abstract.html
Kerberos: An Authentication Service for Computer Networks, B.
Clifford Neuman and Theodore Ts'o
http://nii.isi.edu/publications/kerberos-neuman-tso.html
Evolving Algebras: An Introductory Tutorial, Yuri Gurevich
http://research.microsoft.com/~gurevich/Opera/92.ps
Zero-One Laws, Yuri Gurevich
http://research.microsoft.com/~gurevich/Opera/95.ps
Jensen, F. V. (1996), An introduction to Bayesian networks
(Book)
http://www.cs.auc.dk/research/DSS/abstracts/jensen:96a.html
Jensen, F. V. (1996), Bayesian network basics Bayesian Networks
http://www.cs.auc.dk/research/DSS/abstracts/jensen:96b.html
Dynamic graph algorithms. D. Eppstein,
Z. Galil, and G.F. Italiano.
Tech. Rep. CS96-11, Univ. Ca' Foscari di Venezia, Oct. 1996.
http://www.info.uniroma2.it/~italiano/Papers/dyn-survey.ps.Z
A Mathematical Theory of Communication by Claude E. Shannon, 1948
http://cm.bell-labs.com/cm/ms/what/shannonday/paper.html
A Brief Tutorial on: Information Theory, Excess Entropy and Statistical
Complexity:
Discovering and Quantifying Statistical Structure , David Feldman
http://hornacek.coa.edu/dave/Tutorial/index.html
Discovering Non-Critical Organization: Statistical Mechanical, Information
Theoretic,
and Computational Views of Patterns in One-Dimensional Spin, David
Feldman
http://hornacek.coa.edu/dave/Publications/DNCO.html
M. Anthony, Probabilistic Analysis of Learning in Artificial Neural
Networks: The PAC Model and its Variants
ftp://ftp.icsi.berkeley.edu/pub/ai/jagota/vol1_1.ps.gz
A. Hyvarinen, Survey on Independent Component Analysis
ftp://ftp.icsi.berkeley.edu/pub/ai/jagota/vol2_4.ps.gz
Game Theory: An Introductory Sketch , Roger A. McCain
http://www.coba.drexel.edu/economics/mccain/game/intro.html
INFORMATION RETRIEVAL, (Online Book) C. J. van RIJSBERGEN
http://www.dcs.gla.ac.uk/Keith/Preface.html
``Graphical Models for Probabilistic and Causal Reasoning'' ,
J. Pearl
ftp://ftp.cs.ucla.edu/pub/stat_ser/R236-ACM.ps
Information Theory, Inference and Learning Algorithms (Online Book),
David MacKay
http://wol.ra.phy.cam.ac.uk/mackay/itprnn/
"Inference in Belief Networks: A procedural guide", C. Huang and A.
Darwiche,
http://www.aub.edu.lb/people/darwiche/Papers/ijar95.pdf
we provide a self-contained, procedural guide to understanding and
implementing
Probability Propagation in Trees of Clusters (PPTC). We
synthesize various optimizations to PPTC that are scattered throughout
the literature.
We articulate undocumented, \open secrets" that are vital to producing
a
robust and efficient implementation of PPTC.
Probabilistic Methods in AI, Course Handouts. Nir Friedman,
http://www.cs.huji.ac.il/course/pmai/handouts.html
Slides and links to introductory papers
The Chaos hypertextbook, Glenn Elert
http://www.hypertextbook.com/
Bayesian Model Averaging : A tutorial
Jennifer Hoeting, David Madigan, Adrian Raftery and Chris Volinsky
(1999)
http://www.stat.colostate.edu/~jah/documents/bma2.ps
----------------
Programming with sets
The SETL Programming Language by Robert Dewar
http://birch.eecs.lehigh.edu/~bacon/setlprog.ps.gz
that tutorial has been updated for SETL2 by Robert Hummel,
ftp://cs.nyu.edu/pub/local/hummel/setl2/intro.ps.Z
SETL for Data Processing on the Internet by David Bacon
http://birch.eecs.lehigh.edu/~bacon/survey-pap/index.html
------------
Bretthorst, G. Larry, 1990, ```An Introduction of Parameter Estimation
Using Bayesian Probability,''
http://bayes.wustl.edu/glb/bib.html
http://bayes.wustl.edu/glb/intro.ps.gz
Bretthorst, G. Larry, 1996, ``An Introduction To Model Selection Using
Probability Theory As Logic,''
http://bayes.wustl.edu/glb/bib.html
http://bayes.wustl.edu/glb/model.ps.gz
Bretthorst, G. Larry, 1988, ``Bayesian Spectrum Analysis and Parameter
Estimation,'' (Book)
http://bayes.wustl.edu/glb/book.pdf