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Machine Learning List Vol. 1 No. 12
Machine Learning List: Vol. 1 No. 12
Tuesday, Oct 31, 1989
Contents:
EWSL
Machine Learning Journal Book Reviews
Bibliographies
The Machine Learning List is moderated. Contributions should be relevant to
the scientific study of machine learning. Mail contributions to ml@ics.uci.edu.
Mail requests to be added or deleted to ml-request@ics.uci.edu. Back issues
of Volume 1 may be FTP'd from /usr2/spool/ftp/pub/ml-list/V1/<N> or N.Z where
N is the number of the issue; Host ics.uci.edu; Userid & password: anonymous
----------------------------------------------------------------------
Date: Tue, 31 Oct 89 15:38 GMT
From: CNNNGHMP%vax1.tcd.ie@CUNYVM.CUNY.EDU
Subject: EWSL
Do you know the date for the next EWSL or do you have an address
(preferebly E-Mail) for one of the organisers?
Any information will be appreciated.
Padraig Cunningham
Hitachi Dublin Laboratory
O'Reilly Institute
Trinity College
Dublin 2
Ireland.
----------------------------------------------------------------------
Subject: ewsl
Date: Tue, 31 Oct 89 11:12:28 -0800
From: Ruediger Wirth <wirth@ICS.UCI.EDU>
Hello,
EWSL 89 takes place on December 4-6, 1989 at Montpellier, France. For details
you should probably contact the organizers
Joel Quinquet
e-mail:quinquet@seti.inria.fr.uucp
or
Jean Sallantin
CRIM
1860 rue de Saint-Priest
34100 Montpellier, France
phone: 67 63 04 60
The program chairman is
Katharina Morik
GMD
Postfach 1240
D-5205 Sankt Augustin,
West Germany
e-mail: morik@gmdzi.uucp
I am sure you can obtain further information from one of these addresses.
Ruediger Wirth
----------------------------------------------------------------------
From: "Alberto M. Segre" <segre@cs.cornell.EDU>
Date: Tue, 31 Oct 89 17:21:35 EST
Subject: Machine Learning Journal Book Reviews
Announcement:
Machine Learning Journal Book Reviews
The Machine Learning Journal is soliciting volunteer
reviewers for recent monographs in machine learning. The Journal
intends to include short archival-quality reviews of current
books in the field. Once selected, reviewers will be supplied
with the book of their choosing, and will be expected to produce
a three to five page review for the Journal. Authors will be
given an opportunity to read the review and respond in print.
The ideal reviewer should be a machine-learning researcher,
junior faculty, or advanced graduate student. In any case,
reviewers should have some previous exposure to the field and
will be asked to provide a short statement relating their
qualifications to review a particular book.
Books selected for review should be recent (within the last
five years) releases relevent to the field of machine learning. A
representative listing of books available for review is included
below. For more information, contact:
Alberto Segre segre@cs.cornell.edu
Department of Computer Science tel (607) 255-9196
Cornell University fax (607) 255-4428
Upson Hall
Ithaca, NY 14853-7501
Sample Titles Available for Review
D.H. Ackley, A Connectionist Machine for Genetic
Hillclimbing, Kluwer Academic Publishers, Hingham, MA, 1987.
P. Benjamin, Change of Representation and Inductive Bias,
Kluwer Academic Publishers, Hingham, MA, in press.
G. DeJong, Investigating Explanation-Based Learning, Kluwer
Academic Publishers, Hingham, MA, in press.
E.A. Durfee, Coordination of Distributed Problem Solvers,
Kluwer Academic Publishers, Hingham, MA, 1988.
R. Goebel, DLOG: A Logic-Based Data Model for the Machine
Representation of Knowledge, Kluwer Academic Publishers,
Hingham, MA, in press.
D. Goldberg, The Foundations of Genetic Algorithms, Kluwer
Academic Publishers, Hingham, MA, in press.
Y. Kodratoff, Introduction to Machine Learning, Morgan
Kaufmann Publishers, San Mateo, CA, 1988.
P.D. Laird, Learning from Good and Bad Data, Kluwer Academic
Publishers, Hingham, MA, 1988.
J. Laird, P. Rosenbloom and A. Newell, Universal Subgoaling
and Chunking of Goal Hierarchies, Kluwer Academic
Publishers, Hingham, MA, 1986.
S.L. Marcus, J. McDermott, G.S. Kahn, L. Eshelman, G.
Klinker, D. Offutt and J. Bachant, Automated Knowledge
Acquisition for Expert Systems, Kluwer Academic Publishers,
Hingham, MA, 1988.
S. Minton, Learning Search Control Knowledge, An Explanation
Based Approach, Kluwer Academic Publishers, Hingham, MA,
1988.
R.J. Mooney, A General Explanation-based Learning Mechanism
and its Application to Narrative Understanding, Morgan
Kaufmann Publishers, San Mateo, CA, 1989.
M.A. Musen, Automated Generation of Model-based Knowledge-
Acquisition Tools, Morgan Kaufmann Publishers, San Mateo,
CA, 1989.
P.E. Utgoff, Machine Learning of Inductive Bias, Kluwer
Academic Publishers, Hingham, MA, 1986.
----------------------------------------------------------------------
From: "Alberto M. Segre" <segre@cs.cornell.EDU>
Subject: Bibliographies
Date: Tue, 31 Oct 89 17:21:35 EST
I've put together a bibliography of all of the articles from
ML83, ML85, ML87, ML88, and ML89. These are in BIB/TROFF form (but will
also work for REFER/TROFF). Some string definitions are included as
well; if your bibliographic package doesn't expand abbreviations (e.g.,
REFER), you might want to expand them in the files themselves. If you
are a TEX user, you can probably convert them relatively easily to
BIBTEX form. Enjoy!
-alberto
[Be careful when cutting, two bibliograph files follow. They start
with #----cut here ...
and end with exit 0
Thanks Alberto]
# This is a shell archive.
# Remove everything above and including the cut line.
# Then run the rest of the file through sh.
#----cut here-----cut here-----cut here-----cut here----#
#!/bin/sh
# shar: Shell Archiver
# Run the following text with /bin/sh to create:
# bibinc.ml
# ml83.ref
# ml85.ref
# ml87.ref
# ml88.ref
# ml89.ref
# This archive created: Tue Oct 31 15:55:58 1989
cat << \SHAR_EOF > bibinc.ml
#
# publishers
#
D NYC New York, N\&Y
D PROC Proceedings
D INTL International
#
D MORGAN Morgan Kaufmann Publishers\
%C San Mateo, C\&A
#
# conferences
#
D ML83 PROC of the Second INTL Machine Learning Workshop\
%C Urbana, IL\
%D JUN 1983
#
D ML85 PROC of the Third INTL Machine Learning Workshop\
%C Skytop, PA\
%D JUN 1985
#
D ML87 PROC of the Fourth INTL Machine Learning Workshop\
%E P. Langley\
%I MORGAN\
%C Irvine, CA\
%D JUN 1987
#
D ML88 PROC of the Fifth INTL Machine Learning Conference\
%E J. Laird\
%I MORGAN\
%C Ann Arbor, MI\
%D JUN 1988
#
D ML89 PROC of the Sixth INTL Machine Learning Workshop\
%E A. M. Segre\
%I MORGAN\
%C Ithaca, NY\
%D JUN 1989
SHAR_EOF
cat << \SHAR_EOF > ml83.ref
%T Learning by Augmenting Rules and Accumulating Censors
%A P.H. Winston
%J ML83
%P 2-11
%T Derivational Analogy in Problem Solving and Knowledge Acquisition
%A J.G. Carbonell
%J ML83
%P 12-18
%T Concept Formation by Incremental Analogical Reasoning and Debugging
%A M.H. Burstein
%J ML83
%P 19-25
%T Programming by Analogy
%A N. Dershowitz
%J ML83
%P 26-31
%T Reasoning by Analogy in Scientific Theory Construction
%A L. Darden
%J ML83
%P 32-40
%T Discovering Patterns in Sequences of Objects
%A T.G. Dietterich
%A R.S. Michalski
%J ML83
%P 41-57
%T Learning From Noisy Data
%A J.R. Quinlin
%J ML83
%P 58-64
%T Inductive Rule Generation in the Context of the Fifth Generation
%A D. Michie
%J ML83
%P 65-70
%T Knowledge Acquisition and Learning in EXPERT
%A C.A. Kulikowski
%J ML83
%P 71-73
%T Hierarchical Memories: An Aid to Concept Learning
%A C. Sammut
%A R. Banerji
%J ML83
%P 74-80
%T Learning As A Non-Deterministic But Exact Logical Process
%A Y. Kodratoff
%A J-G. Ganascia
%J ML83
%P 81-91
%T Escaping Brittleness
%A J.H. Holland
%J ML83
%P 92-95
%T Two Programs For Testing Hypotheses Of Any Logical Form
%A C. Glymour
%A K. Kelly
%A R. Scheines
%J ML83
%P 96-98
%T Learning Equation Solving Methods From Worked Examples
%A B. Silver
%J ML83
%P 99-104
%T Adjusting Bias in Concept Learning
%A P.E. Utgoff
%J ML83
%P 105-109
%T Operationalizing Advice: A Problem-Solving Model
%A J. Mostow
%J ML83
%P 110-116
%T Goal Directed Learning
%A T.M. Mitchell
%A R.M. Keller
%J ML83
%P 117-118
%T Program Synthesis As A Theory Formation Task: Problem Representations And Solution Methods
%A S. Amarel
%J ML83
%P 119-120
%T Mechanisms for Qualitative and Quantitative Discovery
%A P. Langley
%A J. Zytkow
%A H.A. Simon
%A G.L. Bradshaw
%J ML83
%P 121-132
%T Cognitive Economy In a Fluid Task Environment
%A D.B. Lenat
%A F. Hayes-Roth
%A P. Klahr
%J ML83
%P 133-146
%T The Role of Experimentation in Theory Formation
%A T.G. Dietterich
%A B.B. Buchanan
%J ML83
%P 147-155
%T How To Structure Structured Objects
%A R.S. Michalski
%A R.E. Stepp
%J ML83
%P 156-160
%T The Architecture of Jumbo
%A D.R. Hofstadter
%J ML83
%P 161-170
%T An Approach to Learning From Observation
%A G. DeJong
%J ML83
%P 171-176
%T Concept Learning in a Rich Input Domain
%A M. Lebowitz
%J ML83
%P 177-182
%T The Chunking of Goal Hierarchies: A Generalized Model of Practice
%A P.S. Rosenbloom
%A A. Newell
%J ML83
%P 183-197
%T Learning Physical Domains: Towards a Theoretical Framework
%A K.D. Forbus
%A D. Gentner
%J ML83
%P 198-202
%T Knowledge Compilation: The General Learning Mechanism
%A J.R. Anderson
%J ML83
%P 203-212
%T Linear Separability and Concept Naturalness
%A D.L. Medin
%J ML83
%P 213-217
%T How Can CHILD Learn About Agreement? Explorations of CHILD's Syntactic Inadequacies
%A M. Selfridge
%J ML83
%P 218-220
%T Inferring (MAL) Rules From Pupils Protocols
%A D. Sleeman
%J ML83
%P 221-227
%T Domain-specific Learning and the Subset Principle
%A R.C. Berwick
%J ML83
%P 228-233
%T Validating A Theory of Human Skill Acquisition
%A K. VanLehn
%J ML83
%P 234
SHAR_EOF
cat << \SHAR_EOF > ml85.ref
%T JUDGE: A Case-Based Reasoning System
%A W. Bain
%J ML85
%P 1-4
%T Learning by Disjunctive Spanning
%A G. Bradshaw
%J ML85
%P 5-7
%T Transfer of Knowledge Between Teaching and Learning Systems
%A P. Brazdil
%J ML85
%P 8-10
%T Analogical Learning with Multiple Models
%A M. Burstein
%J ML85
%P 11-13
%T The World Modelers Project: Objectives and Simulator Architecture
%A J.G. Carbonell
%A G. Hood
%J ML85
%P 14-16
%T The Acquisition of Procedural Knowledge through Inductive Learning
%A K. Chen
%J ML85
%P 17-18
%T Learning Static Evaluation Functions by Linear Regression
%A J. Christensen
%J ML85
%P 19-21
%T Plan Invention and Plan Transformation
%A G. Collins
%J ML85
%P 22-25
%T A Brief Overview of Explanatory Schema Acquisition
%A G. DeJong
%J ML85
%P 26-28
%T The EG Project: Recent Progress
%A T. Dietterich
%J ML85
%P 29-31
%T Functional Properties and Concept Formation
%A J.D. Easterlin
%J ML85
%P 32-34
%T Explanation-Based Learning in Logic Circuit Design
%A T. Ellman
%J ML85
%P 35-37
%T A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects
%A D. Fisher
%J ML85
%P 38-40
%T Exploiting Functional Vocabularies to Learn Structural Descriptions
%A N. Flann
%A T. Dietterich
%J ML85
%P 41-43
%T Combining Numeric and Symbolic Learning Techniques
%A R. Granger Jr.
%A J. Schlimmer
%J ML85
%P 44-49
%T Learning by Understanding Analogies
%A R. Greiner
%J ML85
%P 50-52
%T Analogical Reasoning in the Context of Acquiring Problem Solving Expertise
%A R. Hall
%J ML85
%P 53-55
%T Planning and Learning in a Design Domain: The Problems of Goal and Plan Interactions
%A K. Hammond
%J ML85
%P 56-59
%T Inference of Incorrect Operators
%A H. Hirsh
%A D. Sleeman
%J ML85
%P 60-62
%T A Conceptual Framework for Concept Identification
%A R. Holte
%J ML85
%P 63-66
%T Neural Modeling as One Approach to Machine Learning
%A G. Hood
%J ML85
%P 67-69
%T Steps Toward Building a Dynamic Memory
%A L. Hunter
%J ML85
%P 70-73
%T Learning by Composition
%A G. Iba
%J ML85
%P 74-76
%T Knowledge Acquisition: Investigations and General Principles
%A G. Kahn
%J ML85
%P 77-79
%T Purpose-Directed Analogy: A Summary of Current Research
%A S. Kedar-Cabelli
%J ML85
%P 80-83
%T Development of a Contextual Concept Learning Framework
%A R. Keller
%J ML85
%P 84-87
%T A Model of Acquiring Problem Solving Expertise
%A D. Kibler
%A R. Hall
%J ML85
%P 88-90
%T Inductive Inference and Symbolic Process Prediction
%A H. Ko
%J ML85
%P 91-92
%T Learning at LRI Orsay
%A Y. Kodratoff
%J ML85
%P 93-95
%T Machine Learning Research at Georgia Tech: Using Experience as a Guide for Problem Solving
%A J. Kolodner
%A R. Simpson
%J ML85
%P 96-99
%T Heuristics as Invariants and its Application to Learning
%A R. Korf
%J ML85
%P 100-103
%T Components of Learning in a Reactive Environment
%A P. Langley
%A D. Kibler
%A R. Granger
%J ML85
%P 104-106
%T Learning through Interaction: One Pathway to Mastery
%A R. Lawler
%J ML85
%P 107-109
%T Complex Learning Environments: Hierarchies and the Use of Explanation
%A M. Lebowitz
%J ML85
%P 110-112
%T Predictive Mechanisms
%A A. MacDonald
%J ML85
%P 113-115
%T Knowledge Repair Mechanisms: Evolution Versus Revolution
%A R. Michalski
%J ML85
%P 116-119
%T Overview of the PRODIGY Learning Apprentice
%A S. Minton
%J ML85
%P 120-122
%T A Learning Apprentice System for VLSI Design
%A T. Mitchell
%A S. Mahadevan
%A L. Steinberg
%J ML85
%P 123-125
%T Generalizing Explanations of Narratives into Schemata
%A R. Mooney
%J ML85
%P 126-128
%T Some Requirements for Effective Replay of Derivations
%A J. Mostow
%J ML85
%P 129-132
%T An Architecture for Experiential Learning
%A M. Mozer
%A K. Gross
%J ML85
%P 133-136
%T Research Sumary
%A I. Mozetic
%J ML85
%P 137-139
%T Learning Concepts with a Prototype-Based Model for Concept Representation
%A D. Nagel
%J ML85
%P 140-142
%T Recent Progress on the ''Mathematician's Apprentice'' Project
%A P. O'Rorke
%J ML85
%P 143-145
%T Using and Revising Learned Concept Models: A Research Proposal
%A B. Porter
%J ML85
%P 146-148
%T Conceptual Knowledge Acquisition through Directed Experimentation
%A S. Rajamoney
%J ML85
%P 149-151
%T Goal-Free Learning by Analogy
%A A. Rappaport
%J ML85
%P 152-154
%T A Scientific Approach to Practical Induction
%A L. Rendell
%J ML85
%P 155-158
%T Automating Shift in Representation
%A P. Riddle
%J ML85
%P 159-162
%T Current Research on Learning in SOAR
%A P. Rosenbloom
%A J. Laird
%A A. Newell
%A A. Golding
%A A. Unruh
%J ML85
%P 163-172
%T Learning Concepts in a Complex Robot World
%A C. Sammut
%A D. Hume
%J ML85
%P 173-176
%T Learning State Evaluation Functions
%A P. Schooley
%J ML85
%P 177-179
%T Learning From Data with Errors
%A J. Segen
%J ML85
%P 180-182
%T Explanation-Based Manipulator Learning
%A A.M. Segre
%J ML85
%P 183-185
%T Learning Classical Physics
%A J. Shavlik
%J ML85
%P 186-188
%T Learning Control Information
%A B. Silver
%J ML85
%P 189-191
%T An Investigation of the Nature of Mathematical Discovery
%A M. Sims
%J ML85
%P 192-198
%T Conceptual Clustering of Structured Objects
%A R. Stepp
%J ML85
%P 199-201
%T Learning in Intractable Domains
%A P. Tadepalli
%J ML85
%P 202-205
%T On Compiling Justifiable Models of a Design Domain
%A C. Tong
%J ML85
%P 206-209
%T Learning Heuristic Rules from Deep Reasoning
%A W. van de Velde
%J ML85
%P 213-215
%T A Summary of Current Research
%A K. VanLehn
%J ML85
%P 216-218
%T What Can Be Learned?
%A L.G. Valiant
%J ML85
%P 210-212
%T Learning in Implementation Rules With Operating-Conditions Depending on States in VLSI Design
%A M. Watanabe
%J ML85
%P 219-220
%T Odysseus: A Learning Apprentice
%A D. Wilkins
%A W. Clancey
%A B. Buchanan
%J ML85
%P 221-223
%T Learning From Exceptions to Constraints in Databases
%A K. Williamson
%J ML85
%P 224-226
%T Learning Apprentice Systems Research at Schlumberger
%A H. Winston
%A R. Smith
%A T. Mitchell
%A B. Buchanan
%J ML85
%P 227-229
%T Learning Phrases in Context
%A U. Zernik
%A M. Dyer
%J ML85
%P 230-232
SHAR_EOF
cat << \SHAR_EOF > ml87.ref
%T Learning About Speech Sounds: The NEXUS Project
%A G. Bradshaw
%J ML87
%P 1-11
%T PROTOS: An Exemplar-Based Learning Apprentice
%A E.R. Bareiss
%A B.W. Porter
%J ML87
%P 12-23
%T Learning Representative Exemplars of Concepts: An Initial Case Study
%A D. Kibler
%A D.W. Aha
%J ML87
%P 24-30
%T Decision Trees as Probabilistic Classifiers
%A J.R. Quinlin
%J ML87
%P 31-37
%T Conceptual Clustering, Learning From Examples, and Inference
%A D.H. Fisher
%J ML87
%P 38-49
%T How To Learn Imprecise Concepts: A Method for Employing a Two-Tiered Knowledge Representation in Learning
%A R.S. Michalski
%J ML87
%P 50-58
%T Quasi-Darwinian Learning in a Classifier System
%A S.W. Wilson
%J ML87
%P 59-65
%T More Robust Concept Learning Using Dynamically-Variable Bias
%A L. Rendell
%A R. Seshu
%A D. Tcheng
%J ML87
%P 66-78
%T Incremental Adjustment of Representations for Learning
%A J.C. Schlimmer
%J ML87
%P 79-90
%T Concept Learning in Context
%A R.M. Keller
%J ML87
%P 91-102
%T Strategy Learning with Multilayer Connectionist Representations
%A C.W. Anderson
%J ML87
%P 103-114
%T Learning A Preference Predicate
%A P.E. Utgoff
%A S. Saxena
%J ML87
%P 115-121
%T Acquiring Effective Search Control Rules: Explanation-Based Learning in the PRODIGY System
%A S. Minton
%A J.G. Carbonell
%A O. Etzioni
%J ML87
%P 122-133
%T The Anatomy of a Weak Learning Method for Use in Goal Directed Search
%A T.L. McCluskey
%J ML87
%P 134-140
%T Learning and Reusing Explanations
%A K.J. Hammond
%J ML87
%P 141-147
%T LT Revisited: Experimental Results of Applying Explanation-Based Learning to the Logic of Principia Mathematica
%A P. O'Rorke
%J ML87
%P 148-159
%T What Is an Explanation in DISCIPLE?
%A Y. Kodratoff
%A G. Tecuci
%J ML87
%P 160-166
%T Extending Problem Solver Capabilities Through Case-Based Inference
%A J.L. Kolodner
%J ML87
%P 167-178
%T Learning to Integrate Syntax and Semantics
%A W.G. Lehnert
%J ML87
%P 179-190
%T How Do Machine-Learning Paradigms Fare in Language Acquisition?
%A U. Zernik
%J ML87
%P 191-197
%T The Acquisition of Polysemy
%A J.H. Martin
%J ML87
%P 198-204
%T Cirrus: An Automated Protocol Analysis Tool
%A K. VanLehn
%A S. Garlick
%J ML87
%P 205-217
%T Scientific Theory Formation Through Analogical Inference
%A B. Falkenhainer
%J ML87
%P 218-229
%T Inducing Casual and Social Theories: A Prerequisite for Explanation-based Learning
%A M.J. Pazzani
%J ML87
%P 230-241
%T The Role of Abstractions in Learning Qualitative Models
%A I. Mozetic
%J ML87
%P 242-255
%T Learning by Experimentation
%A J.G. Carbonell
%A Y. Gil
%J ML87
%P 256-266
%T Observation and Generalisation in a Simulated Robot World
%A C. Sammut
%A D. Hume
%J ML87
%P 267-273
%T Empirical and Analytical Discovery in IL
%A M.H. Sims
%J ML87
%P 274-280
%T Combining Many Searches in the FAHRENHEIT Discovery System
%A J.M. Zytkow
%J ML87
%P 281-287
%T Casual Analysis and Inductive Learning
%A J.R. Anderson
%J ML87
%P 288-299
%T Varieties of Learning in SOAR: 1987
%A D.M. Steier
%A J.E. Laird
%A A. Newell
%A P.S. Rosenbloom
%J ML87
%P 300-311
%T Hill-Climbing Theories of Learning
%A P. Langley
%A J.H. Gennari
%A W. Iba
%J ML87
%P 312-323
%T Bias, Version Spaces and Valiants Learning Framework
%A D. Haussler
%J ML87
%P 324-336
%T Recent Results on Boolean Concept Learning
%A M. Kearns
%A M. Li
%A L. Pitt
%A L. Valiant
%J ML87
%P 337-352
%T Machine Learning from Structured Objects
%A R.E. Stepp
%J ML87
%P 353-363
%T A New Approach to Unsupervised Learning in Deterministic Environments
%A R.L. Rivest
%A R.E. Schapire
%J ML87
%P 364-375
%T Searching for Operational Concept Descriptions in BAR, MetaLEX, EBG
%A J. Mostow
%J ML87
%P 376-382
%T Explanation-Based Generalization as Resolution Theorem Proving
%A S.T. Kedar-Cabelli
%A L.T. McCarty
%J ML87
%P 383-389
%T Analogy and Single-Instance Generalization
%A S.J. Russell
%J ML87
%P 390-402
SHAR_EOF
cat << \SHAR_EOF > ml88.ref
%T Using a Generalization Hierarchy to Learn from Examples
%A R.G. Kerber
%J ML88
%P 1-7
%T Tuning Rule-Based Systems to Their Environments
%A H. Tallis
%J ML88
%P 8-14
%T On Asking the Right Questions
%A B.J. Krawchuk
%A I.H. Witten
%J ML88
%P 15-21
%T Concept Simplification and Prediction Accuracy
%A D.H. Fisher
%A J.C. Schlimmer
%J ML88
%P 22-28
%T Learning Graph Models of Shape
%A J. Segen
%J ML88
%P 29-35
%T Learning Categorical Decision Criteria in Biomedical Domains
%A K.A. Spackman
%J ML88
%P 36-46
%T Conceptual Clumping of Binary Vectors with Occam's Razor
%A J. Segen
%J ML88
%P 47-53
%T AutoClass: A Bayesian Classification System
%A P. Cheeseman
%A J. Kelly
%A M. Self
%A J. Stutz
%A W. Taylor
%A D. Freeman
%J ML88
%P 54-64
%T Incremental Multiple Concept Learning Using Experiments
%A K.P. Gross
%J ML88
%P 65-72
%T Trading Off Simplicity and Coverage in Incremental Concept Learning
%A W. Iba
%A J. Wogulis
%A P. Langley
%J ML88
%P 73-79
%T Deferred Commitment in UNIMEM: Waiting to Learn
%A M. Lebowitz
%J ML88
%P 80-86
%T Experiments on the Costs and Benefits of Windowing in ID3
%A J. Wirth
%A J. Catlett
%J ML88
%P 87-99
%T Improved Decision Trees: A Generalized Version of ID3
%A J. Cheng
%A U.M. Fayyad
%A K.B. Irani
%A Z. Qian
%J ML88
%P 100-106
%T ID5: An Incremental ID3
%A P.E. Utgoff
%J ML88
%P 107-120
%T Using Weighted Networks to Represent Classification Knowledge in Noisy Domains
%A M. Tan
%A L. Eshelman
%J ML88
%P 121-134
%T An Empirical Comparison of Genetic and Decision-Tree Classifiers
%A J.R. Quinlan
%J ML88
%P 135-141
%T Population Size in Classifier Systems
%A G.C. Robertson
%J ML88
%P 142-152
%T Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms
%A R. A. Caruana
%A J.D. Schaffer
%J ML88
%P 153-161
%T Classifier Systems with hamming Weights
%A L. Davis
%A D.K. Young
%J Ml88
%P 162-173
%T Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems
%A A.V. Sannier II
%A E.D. Goodman
%J ML88
%P 174-180
%T Some Interesting Properties of a Connectionist Inductive Learning System
%A E.J. Wisniewski
%A J.A. Anderson
%J ML88
%P 181-187
%T Competitive Reinforcement Learning
%A K.J. Lynne
%J Ml88
%P 188-189
%T Connectionist Learning of Expert Backgammon Evaluations
%A G. Tesauro
%J ML88
%P 200-206
%T Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist Approach
%A B.W. Mel
%J ML88
%P 207-213
%T Reasoning About Operationality for Explanation-Based Learning
%A H. Hirsh
%J Ml88
%P 214-220
%T Boundaries of Operationality
%A M.S. Braverman
%A S.J. Russell
%J ML88
%P 221-234
%T On the Tractability of Learning from Incomplete Theories
%A S. Mahadevan
%A P. Tadepalli
%J ML88
%P 235-241
%T Active Explanation Reduction: An Approach to the Multiple Explanations Problem
%A S.A. Rajamoney
%A G.F. DeJong
%J Ml88
%P 242-255
%T Generalizing Number and Learning from Multiple Examples in Explanation Based Learning
%A W.W. Cohen
%J ML88
%P 256-269
%T Generalizing the Order of Operators in Macro-Operators
%A R.J. Mooney
%J ML88
%P 270-283
%T Using Experience-Based Learning in Game Playing
%A K.A. DeJong
%A A.C. Schultz
%J ML88
%P 284-290
%T Integrated Learning with Incorrect and Incomplete Theories
%A M.J. Pazzani
%J ML88
%P 291-297
%T An Approach Based on Integrated Learning to Generating Stories from Stories
%A C. Carpineto
%J Ml88
%P 298-304
%T A Knowledge Intensive Approach to Concept Induction
%A F. Bergadano
%A A. Giordana
%J Ml88
%P 305-317
%T Learning to Program by Examining and Modifying Cases
%A R.S. Williams
%J Ml88
%P 318-324
%T Theory Discovery and the Hypothesis Language
%A K.T. Kelly
%J ML88
%P 325-338
%T Machine Invention of First Order Predicates by Inverting Resolution
%A S. Muggleton
%A W. Buntine
%J ML88
%P 339-352
%T The Interdependencies of Theory Formation, Revision, and Experimentation
%A B. Falkenhainer
%A S. Rajamoney
%J ML88
%P 353-366
%T A Hill-Climbing Approach to Machine Discovery
%A D. Rose
%A P. Langley
%J ML88
%P 367-373
%T Reduction: A Practical Mechanism of Searching for Regularity in Data
%A Y-H Wu
%J ML88
%P 374-380
%T Extending the Valiant Learning Model
%A J. Amsterdam
%J ML88
%P 381-394
%T Learning Systems of First-Order Rules
%A N. Helft
%J ML88
%P 395-401
%T Two New Frameworks for Learning
%A B.K. Natarajan
%A P. Tadepalli
%J ML88
%P 402-415
%T Hypothesis Filtering: A Practical Approach to Reliable Learning
%A O. Etzioni
%J ML88
%P 416-429
%T Diffy-S: Learning Robot Operator Schemata from Examples
%A C.M. Kadie
%J ML88
%P 430-436
%T Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems
%A C. Sammut
%J ML88
%P 437-443
%T Utilizing Experience for Improving the Tactical Manager
%A M.D. Erickson
%A J.M. Zytkow
%J ML88
%P 444-450
%T Some Chunks Are Expensive
%A M. Tambe
%A A. Newell
%J ML88
%P 451-458
%T The Role of Forgetting in Learning
%A S. Markovitch
%A P.D. Scott
%J ML88
%P 459-465
SHAR_EOF
cat << \SHAR_EOF > ml89.ref
%T Unifying Themes in Empirical and Explanation-Based Learning
%A P. Langley
%J ML89
%P 2-4
%T Induction Over the Unexplained: Integrated Learning of Concepts with Both Explainable and Conventional Aspects
%A R. Mooney
%A D. Ourston
%J ML89
%P 5-7
%T Conceptual Clustering of Explanations
%A J.P. Yoo
%A D.H. Fisher
%J ML89
%P 8-10
%T A Tight Integration of Deductive and Inductive Learning
%A G. Widmer
%J ML89
%P 11-13
%T Multi-Strategy Learning in Nonhomogeneous Domain Theories
%A G. Tecuci
%A Y. Kodratoff
%J ML89
%P 14-16
%T A Description of Preference criterion in Constructive Learning: A Discussion of Basic Issues
%A J. Zhang
%A R.S. Michalski
%J ML89
%P 17-19
%T Combining Case-Based Reasoning, Explanation-Based Learning, and Learning from Instruction
%A M. Redmond
%J ML89
%P 20-22
%T Deduction in Top-Down Inductive Learning
%A F. Bergadano
%A A. Giordana
%A S. Ponsero
%J ML89
%P 23-25
%T One-Sided Algorithms for Integrating Empirical and Explanation-Based Learning
%A W.E. Sarrett
%A M.J. Pazzani
%J ML89
%P 26-28
%T Combining Empirical and Analytical Learning with Version Spaces
%A H. Hirsh
%J ML89
%P 29-33
%T Finding New Rules for Incomplete Theories: Explicit Biases for Induction with Contextual Information
%A A.P. Danyluk
%J ML89
%P 34-36
%T Learning From Plausible Explanations
%A T.E. Fawcett
%J ML89
%P 37-39
%T Augmenting Domain Theory for Explanation-Based Generalization
%A K.M. Ali
%J ML89
%P 40-42
%T Explanation Based Learning as Constrained Search
%A D. Haines
%J ML89
%P 43-45
%T Reducing Search and Learning Goal Preferences
%A S. Morris
%J ML89
%P 46-48
%T Adaptation-Based Explanation: Explanations as Cases
%A A. Kass
%J ML89
%P 49-51
%T A Retrieval Model Using Feature Selection
%A C.M. Seifert
%J ML89
%P 52-54
%T Improving Decision-Making on the Basis of Experience
%A B. Krulwich
%A G. Collins
%A L. Birnbaum
%J ML89
%P 55-57
%T Explanation-Based Acceleration of Similarity-Based Learning
%A M. Numao
%A M. Shimura
%J ML89
%P 58-60
%T Knowledge Acquisition Planning: Results and Prospects
%A L. Hunter
%J ML89
%P 61-65
%T Learning by Instruction in Connectionist Systems
%A J. Diederich
%J ML89
%P 66-68
%T Integrating Learning in a Neural Network
%A B.F. Katz
%J ML89
%P 69-71
%T Explanation-Based Learning with Weak Domain Theories
%A M.J. Pazzani
%J ML89
%P 72-74
%T Using Domain Knowledge to Improve Inductive Learning Algorithms for Diagnosis
%A G. Friedrich
%A W. Nejdl
%J ML89
%P 75-77
%T A Framework for Improving Efficiency and Accuracy
%A J. Wogulis
%J ML89
%P 78-80
%T Error Correction in Constructive Induction
%A G. Drastal
%A R. Meunier
%A S. Raatz
%J ML89
%P 81-83
%T Improving Explanation-Based Indexing with Empirical Learning
%A R. Barletta
%A R. Kerber
%J ML89
%P 84-86
%T A Schema for an Integrated Learning System
%A M. Wollowski
%J ML89
%P 87-89
%T Combining Explanation-Based Learning and Artificial Neural Networks
%A J. W. Shavlik
%A G.G. Towell
%J ML89
%P 90-93
%T Learning Classification Rules Using Bayes
%A W. Buntine
%J ML89
%P 94-98
%T New Empirical Learning Mechanisms Perform Significantly Better in Real Life Domains
%A M. Gams
%A A. Karalic
%J ML89
%P 99-103
%T Inductive Learning with BCT
%A P.K. Chan
%J ML89
%P 104-108
%T What Good Are Experiments??
%A R.A. Ruff
%A T.G. Dietterich
%J ML89
%P 109-112
%T An Experimental Comparison of Human and Machine Learning Formalisms
%A S. Muggleton
%A M. Bain
%A J. Hayes-Michie
%A D. Michie
%J ML89
%P 113-118
%T Two Algorithms That Learn DNF by Discovering Relevant Features
%A G. Pagallo
%A D. Haussler
%J ML89
%P 119-123
%T Limitations on Inductive Learning
%A T.G. Dietterich
%J ML89
%P 124-128
%T The Induction of Probabilistic Rule Sets-The Itrule Algorithm
%A R.M. Goodman
%A P. Smyth
%J ML89
%P 129-132
%T Empirical Substructure Discovery
%A L.B. Holder
%J ML89
%P 133-136
%T Learning the Behavior of Dynamical Systems from Examples
%A J. Paredis
%J ML89
%P 137-140
%T Experiments in Robot Learning
%A M.T. Mason
%A A.D. Christiansen
%A T.M. Mitchell
%J ML89
%P 141-145
%T Induction of Decision Trees from Inconclusive Data
%A S. Spangler
%A U.M. Fayyad
%A R. Uthurusamy
%J ML89
%P 146-150
%T Knowledge Intensive Induction
%A M. Manago
%J ML89
%P 151-155
%T An Ounce of Knowledge is Worth a Ton of Data: Quantitative Studies of the Trade-Off Between Expertise and Data Based on Statistically Well-Founded Empirical Induction
%A B.R. Gaines
%J ML89
%P 156-159
%T Signal Detection Theory: Valuable Tools for Evaluating Inductive Learning
%A K.A. Spackman
%J ML89
%P 160-163
%T Unknown Attribute Values in Induction
%A J.R. Quinlan
%J ML89
%P 164-168
%T Processing Issues in Comparisons of Symbolic and Connectionist Learning Systems
%A D. Fisher
%A K. McKusick
%A R. Mooney
%A J.W. Shavlik
%A G. Towell
%J ML89
%P 169=173
%T Bacon, Data Analysis and Artificial Intelligence
%A C. Schaffer
%J ML89
%P 174-179
%T Learning to Plan in Complex Domains
%A D. Ruby
%A D. Kibler
%J ML89
%P 180-182
%T An Empirical Analysis of EBL Approaches for Learning Plan Schemata
%A J.W. Shavlik
%J ML89
%P 183-187
%T Learning Decision Rules for Scheduling Problems: A Classifier Hybrid Approach
%A M.R. Hilliard
%A G. Liepins
%A G. Rangarajan
%A M. Palmer
%J ML89
%P 188-190
%T Learning Tactical Plans for Pilot Aiding
%A K.R. Levi
%A D. Perschbacher
%A V.L. Shalin
%J ML89
%P 191-193
%T Issues in the Justification-Based Diagnosis of Planning Failures
%A L. Birnbaum
%A G. Collins
%A B. Krulwich
%J ML89
%P 194-196
%T Learning Procedural Knowledge in the EBG Context
%A S. Matwin
%A J. Morin
%J ML89
%P 197-199
%T Learning Invariants from Explanations
%A J-F Puget
%J Ml89
%P 200-204
%T Using Learning to Recover Side-Effects of Operators in Robotics
%A R.P. Sobek
%A J-P Laumond
%J ML89
%P 205-208
%T Learning to Recognize Plans Involving Affect
%A P. O'Rorke
%A T. Cain
%A A. Ortony
%J ML89
%P 209-211
%T Learning to Retrieve Useful Information for Problem Solving
%A R. Jones
%J ML89
%P 212-214
%T Discovering Problem Solving Strategies: What Humans Do and Machines Don't (Yet)
%A K. VanLehn
%J ML89
%P 215-217
%T Approximating Learned Search Control Knowledge
%A M.P. Chase
%A M. Zweben
%A R.L. Piazza
%A J.D. Burger
%A P.P. Maglio
%A H. Hirsh
%J ML89
%P 218-220
%T Planning in Games Using Approximately Learned Macros
%A P. Tadepalli
%J ML89
%P 221-223
%T Learning Approximate Plans for Use in the Real World
%A S.W. Bennett
%J ML89
%P 224-228
%T Using Concept Hierarchies to Organize Plan Knowledge
%A J.A. Allen
%A P. Langley
%J ML89
%P 229-231
%T Conceptual Clustering of Mean-Ends Plans
%A H. Yang
%A D.H. Fisher
%J ML89
%P 232-234
%T Learning Appropriate Abstractions for Planning in Formation Problems
%A N.S. Flann
%J ML89
%P 235-239
%T Discovering Admissible Search Heuristics by Abstracting and Optimizing
%A J. Mostow
%A A.E. Prieditis
%J Ml89
%P 240
%T Learning Hierarchies of Abstraction Spaces
%A C.A. Knoblock
%J ML89
%P 241-245
%T Learning from Opportunity
%A T. Converse
%A K. Hammond
%A M. Marks
%J ML89
%P 246-248
%T Learning by Analyzing Fortuitous Occurances
%A S.A. Chien
%J ML89
%P 249-251
%T Explanation-Based Learning of Reactive Operators
%A M.T. Gervasio
%A G.F. DeJong
%J ML89
%P 252-254
%T On Becoming Reactive
%A J. Blythe
%A T.M. Mitchell
%J ML89
%P 255-259
%T Knowledge Base Refinement and Theory Revision
%A A. Ginsberg
%J Ml89
%P 260-265
%T Theory Formation by Abduction: Initial Results of a Case Study Based on the Chemical Revolution
%A P. O'Rorke
%A S. Morris
%A D. Schulenburg
%J ML89
%P 266-271
%T Using Domain Knowledge to Aid Scientific Theory Revision
%A D. Rose
%J ML89
%P 272-277
%T The Role of Experimentation in Scientific Theory Revision
%A D. Kulkarni
%A H.A. Simon
%J ML89
%P 278-283
%T Exemplar-Based Theory Rejection: An Approach to the Experience Consistency Problem
%A S.A. Rajamoney
%J ML89
%P 284-289
%T Controlling Search for the Consequences of New Information During Knowledge Integration
%A K.S. Murray
%A B.W. Porter
%J ML89
%P 290-295
%T Identifying Knowledge Base Deficiencies by Observing User Behavior
%A K.R. Levi
%A V.L. Shalin
%A D.L. Perschbacher
%J ML89
%P 296-301
%T Toward Automated Rational Reconstruction: A Case Study
%A C. Tong
%A P. Franklin
%J ML89
%P 302-307
%T Discovering Mathematical Operator Definitions
%A M.H. Sims
%A J.L. Bresina
%J ML89
%P 308-313
%T Imprecise Concept Learning Within a Growing Language
%A Z.W. Ras
%A M. Zemankova
%J ML89
%P 314-319
%T Using Determinations in EBL: A Solution to the Incomplete Theory Problem
%A S. Mahadevan
%J ML89
%P 320-325
%T Some Results on the Complexity of Knowledge-Base Refinement
%A M. Valtorta
%J ML89
%P 326-331
%T Knowledge Base Refinement as Improving an Incorrect, Inconsistent and Incomplete Domain Theory
%A D.C. Wilkins
%A K-W. Tan
%J ML89
%P 332-339
%T Incremental Learning of Control Strategies with Genetic Algorithms
%A J.J. Grefenstette
%J ML89
%P 340-344
%T Tower of Hanoi with Connectionist Networks: Learning New Features
%A C.W. Anderson
%J ML89
%P 345-349
%T A Formal Framework for Learning in Embedded Systems
%A L.P. Kaelbling
%J ML89
%P 350-353
%T A Role for Anticipation in Reactive Systems that Learn
%A S.D. Whitehead
%A D.H. Ballard
%J ML89
%P 354-357
%T Uncertainty Based Selection of Learning Experiences
%A P.D. Scott
%A S. Markovitch
%J ML89
%P 358-361
%T Improved Training Via Incremental Learning
%A P.E. Utgoff
%J Ml89
%P 362-365
%T Incremental Batch Learning
%A S.H. Clearwater
%A T-P Cheng
%A H. Hirsh
%A B.G. Buchanan
%J ML89
%P 366-370
%T Incremental Concept Formation with Composite Objects
%A K. Thompson
%A P. Langley
%J ML89
%P 371-374
%T Using Multiple Representations to Improve Inductive Bias: Gray and Binary Coding for Genetic Algorithms
%A R.A. Caruana
%A J.D. Schaffer
%A L.J. Eshelman
%J ML89
%P 375-378
%T Focused Concept Formation
%A J.H. Gannari
%J ML89
%P 379-382
%T An Exploration Into Incremental Learning: The INFLUENCE System
%A A. Cornuejols
%J ML89
%P 383-386
%T Incremental, Instance-Based Learning of Independent and Graded Concept Descriptions
%A D.W. Aha
%J ML89
%P 387-391
%T Cost-Sensitive Concept Learning of Sensor Use in Approach and Recognition
%A M. Tan
%A J.C. Schlimmer
%J ML89
%P 392-395
%T Reducing Redundant Learning
%A J.D. Martin
%J ML89
%P 396-399
%T Incremental Clustering by Minimizing Representation Length
%A J. Segen
%J ML89
%P 400-403
%T Information Filters and Their Implementation in the SYLLOG System
%A S. Markovitch
%A P.D. Scott
%J ML89
%P 404-407
%T Adaptive Learning of Decision-Theoretic Search Control Knowledge
%A E.H. Wefald
%A S.J. Russell
%J ML89
%P 408-411
%T Adaptive Strategies of Learning: A Study of Two-Person Zero-Sum Competition
%A O. Selfridge
%J ML89
%P 412-415
%T An Incremental Genetic Algorithm for Real-Time Learning
%A T.C. Fogarty
%J ML89
%P 416-419
%T Participatory Learning: A Constructivist Model
%A R.R. Yager
%A K.M. Ford
%J ML89
%P 420-425
%T Representational Issues in Machine Learning
%A D. Subramanian
%J ML89
%P 426-429
%T Labor Saving New Distinctions
%A J. Woodfill
%J ML89
%P 430-433
%T A Theory of Justified Reformulations
%A D. Subramanian
%J ML89
%P 434-438
%T Reformulation from State Space to Reduction Space
%A P.J. Riddle
%J ML89
%P 439-440
%T Knowledge-Based Feature Generation
%A J.P. Callan
%J ML89
%P 441-443
%T Enriching Vocabularies by Generalizing Explanation Structures
%A R. Maclin
%A J.W. Shavlik
%J ML89
%P 444-446
%T Higher-Order and Modal Logic as a Framework for Explanation-Based Generalization
%A S. Dietzen
%A F. Pfenning
%J ML89
%P 447-449
%T Towards a Formal Analysis of EBL
%A R. Greiner
%J ML89
%P 450-453
%T A Mathematical Framework for Studying Representation
%A R.C. Holte
%A R.M. Zimmer
%J ML89
%P 454-456
%T Refining Representations to Improve Problem Solving Quality
%A J.C. Schlimmer
%J ML89
%P 457-460
%T Comparing Systems and Analyzing Functions to Improve Constructive Induction
%A L. Rendell
%J ML89
%P 461-464
%T Evaluating Alternative Instance Representations
%A S. Saxena
%J ML89
%P 465-468
%T Evaluating Bias During Pac-Learning
%A L. Chrisman
%J ML89
%P 469-471
%T Constructing Representations Using Inverted Spaces
%A P. Mehra
%J ML89
%P 472-473
%T A Constructive Induction Framework
%A C.J. Matheus
%J ML89
%P 474-475
%T Constructive Induction by Analogy
%A L. De Raedt
%A M. Bruynooghe
%J ML89
%P 476-477
%T Concept Discovery Through Utilization of Invariance Embedded in the Description Language
%A M.M. Kokar
%J ML89
%P 478-479
%T Declarative Bias for Structural Domains
%A B.N. Grosof
%A S.J. Russell
%J Ml89
%P 480-482
%T Automatic Construction of a Hierarchical Generate-and-Test Algorithm
%A S. Mohan
%A C. Tong
%J ML89
%P 483-484
%T A Knowledge-Level Analysis of Informing
%A J. Y-J. Hsu
%J ML89
%P 485-488
%T An Object-Oriented Representation for Search Algorithms
%A J. Mostow
%J ML89
%P 489-491
%T Compiling Learning Vocabulary from a Performance System Description
%A R.M. Keller
%J ML89
%P 492-495
%T Generalized Recursive Splitting Algorithms for Learning Hybrid Concepts
%A B. Lambert
%A D. Tcheng
%A S. C-Y Lu
%J ML89
%P 496-498
%T Screening Hypotheses with Explicit Bias
%A D. Gordon
%J ML89
%P 499-500
%T Building a Learning Bias from Perceived Dependencies
%A Ch. deSainte Marie
%J Ml89
%P 501-502
%T A Bootstrapping Approach to Conceptual Clustering
%A K. Morik
%A J-U Kietz
%J ML89
%P 503-504
%T Overcoming Feature Space Bias in a Reactive Environment
%A H. Tallis
%J ML89
%P 505-508
SHAR_EOF
# End of shell archive
exit 0
----------------------------------------------------------------------
From: "Alberto M. Segre" <segre@cs.cornell.EDU>
Subject: Bibliographies
Date: Tue, 31 Oct 89 17:21:35 EST
This is the contents of the two ML an
AI approach volumes, again in BIB/TROFF or REFER/TROFF form.
# This is a shell archive.
# Remove everything above and including the cut line.
# Then run the rest of the file through sh.
#----cut here-----cut here-----cut here-----cut here----#
#!/bin/sh
# shar: Shell Archiver
# Run the following text with /bin/sh to create:
# bibinc.ml2
# mlv12.ref
# This archive created: Tue Oct 31 17:20:28 1989
cat << \SHAR_EOF > bibinc.ml2
#
# books
#
D MLV1 Machine Learning: An AI Approach\
%E R.S. Michalski, J.G. Carbonell, and T.M. Mitchell\
%V 1\
%I MORGAN\
%D 1983
#
D MLV2 Machine Learning: An AI Approach\
%E R.S. Michalski, J.G. Carbonell, and T.M. Mitchell\
%V 2\
%I MORGAN\
%D 1986
SHAR_EOF
cat << \SHAR_EOF > mlv12.ref
%A An Overview of Machine Learning
%A J.G. Carbonell
%A R.S. Michalski
%A T.M. Mitchell
%B MLv1
%P 3-24
%T Why Should Machines Learn?
%A H.A. Simon
%B MLv1
%P 25-38
%T A Comparative Review of Selected Methods for Learning from Examples
%A T.G. Dietterich
%A R.S. Michalski
%B MLv1
%P 41-82
%T A Theory and Methodology of Inductive Learning
%A R.S. Michalski
%B MLv1
%P 83-134
%T Learning by Analogy: Formulating and Generalizing Plans from Past Experience
%A J.G. Carbonell
%B MLv1
%P 137-162
%T Learning by Experimentation: Acquiring and Refining Problem-Solving Heuristics
%A T.M. Mitchell
%A P.E. Utgoff
%A R. Banerji
%B MLv1
%P 163-190
%T Acquisition of Proof Skills in Geometry
%A J.R. Anderson
%B MLv1
%P 191-220
%T Using Proofs and Refutations to Learn from Experience
%A F. Hayes-Roth
%B MLv1
%P 221-240
%T The Role of Heuristics in Learning by Discovery: Three Case Studies
%A D. B. Lenat
%B MLv1
%P 243-306
%T Rediscovering Chemistry With the BACON System
%A P. Langley
%A G.L. Bradshaw
%A H.A. Simon
%B MLv1
%P 307-330
%T Learning from Observation: Conceptual Clustering
%A R.S. Michalski
%A R.E. Stepp
%B MLv1
%P 331-364
%T Machine Transformation of Advice into a Heuristic Search Procedure
%A D.J. Mostow
%B MLv1
%P 367-404
%T Learning by Being Told: Acquiring Knowledge for Information Management
%A N. Haas
%A G.G. Hendrix
%B MLv1
%P 405-428
%T The Instructable Production System: A Retrospective Analysis
%A M.D. Rychener
%B MLv1
%P 429-460
%T Learning Efficient Classification Procedures and their Application to Chess End Games
%A J.R. Quinlin
%B MLv1
%P 463-482
%T Inferring Student Models for Intelligent Computer-Aided Instruction
%A D.H. Sleeman
%B MLv1
%P 483-510
%T Understanding the Nature of Learning: Issues and Research Directions
%A R.S. Michalski
%B MLv2
%P 3-26
%T Machine Learning: Challenges of the Eighties
%A R.S. Michalski
%A S. Amarel
%A D.B. Lenat
%A D. Michie
%A P. Winston
%B MLv2
%P 27-42
%T Learning by Augmenting Rules and Accumulating Censors
%A P.H. Winston
%B MLv2
%P 45-62
%T Learning to Predict Sequences
%A T.G. Dietterich
%A R.S. Michalski
%B MLv2
%P 63-106
%T Shift of Bias for Inductive Concept Learning
%A P.E. Utgoff
%B MLv2
%P 107-148
%T The Effect of Noise on Concept Learning
%A J.R. Quinlin
%B MLv2
%P 149-166
%T Learning Concepts by Asking Questions
%A C. Sammut
%A R.B. Banerji
%B MLv2
%P 167-192
%T Concept Learning in a Rich Input Domain: Generalization-Based Memory
%A M. Lebowitz
%B MLv2
%P 193-214
%T Improving the Generalization Step in Learning
%A Y. Kodratoff
%A J-G. Ganascia
%B MLv2
%P 215-244
%T The Chunking of Goal Hierarchies: A Generalized Model of Practice
%A P.S. Rosenbloom
%A A. Newell
%B MLv2
%P 247-288
%T Knowledge Compilation: The General Learning Mechanism
%A J.R. Anderson
%B MLv2
%P 289-310
%T Learning Physical Domains
%A K.D. Forbus
%A D. Gentner
%B MLv2
%P 311-348
%T Concept Formation by Incremental Analogical Reasoning and Debugging
%A M.H. Burstein
%B MLv2
%P 351-370
%T Derivational Analogy: A Theory of Reconstructive Problem Solving and Expertise Acquisition
%A J.G. Carbonell
%B MLv2
%P 371-392
%T Programming by Analogy
%A N. Dershowitz
%B MLv2
%P 393-422
%T The Search for Regularity: Four aspects of Scientific Discovery
%A P. Langley
%A J. Zytkow
%A H.A. Simon
%A G.L. Bradshaw
%B MLv2
%P 425-470
%T Conceptual Clustering: Inventing Goal-Oriented Classifications of Structured Objects
%A R.E. Stepp
%A R.S. Michalski
%B MLv2
%P 471-498
%T Program Synthesis as a Theory Formation Task: Problem Representations and Solution Methods
%A S. Amarel
%B MLv2
%P 499-570
%T An Approach to Learning from Observation
%A G. DeJong
%B MLv2
%P 571-590
%T Escaping Brittleness: The Possibilities of General-Purpose Learning Algorithms Applied to Parallel Rule-Based Systems
%A J. H. Holland
%B MLv2
%P 593-624
%T Learning From Positive-Only Examples: The Subset Principle and Three Case Studies
%A R.C. Berwick
%B MLv2
%P 625-646
%T Precondition Analysis: Learning Control Information
%A B. Silver
%B MLv2
%P 647-670
SHAR_EOF
# End of shell archive
exit 0
----------------------------------------------------------------------
END of ML-LIST 1.12
