Machine Learning: A Guide to Current Research

Pirmais vāks
Tom M. Mitchell, Jaime G. Carbonell, Ryszard S. Michalski
Springer Science & Business Media, 1986. gada 30. apr. - 429 lappuses
One of the currently most active research areas within Artificial Intelligence is the field of Machine Learning. which involves the study and development of computational models of learning processes. A major goal of research in this field is to build computers capable of improving their performance with practice and of acquiring knowledge on their own. The intent of this book is to provide a snapshot of this field through a broad. representative set of easily assimilated short papers. As such. this book is intended to complement the two volumes of Machine Learning: An Artificial Intelligence Approach (Morgan-Kaufman Publishers). which provide a smaller number of in-depth research papers. Each of the 77 papers in the present book summarizes a current research effort. and provides references to longer expositions appearing elsewhere. These papers cover a broad range of topics. including research on analogy. conceptual clustering. explanation-based generalization. incremental learning. inductive inference. learning apprentice systems. machine discovery. theoretical models of learning. and applications of machine learning methods. A subject index IS provided to assist in locating research related to specific topics. The majority of these papers were collected from the participants at the Third International Machine Learning Workshop. held June 24-26. 1985 at Skytop Lodge. Skytop. Pennsylvania. While the list of research projects covered is not exhaustive. we believe that it provides a representative sampling of the best ongoing work in the field. and a unique perspective on where the field is and where it is headed.

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LibraryThing Review

Lietotāja recenzija  - IvanIdris - LibraryThing

This is an introductory book on Machine Learning. There is quite a lot of mathematics and statistics in the book, which I like. A large number of methods and algorithms are introduced: Neural Networks ... Lasīt pilnu pārskatu

Saturs

A CASEBASED REASONING SYSTEM
1
LEARNING BY DISJUNCTIVE SPANNING
11
SOME APPROACHES TO KNOWLEDGE ACQUISITION
19
ANALOGICAL LEARNING WITH MULTIPLE MODELS
25
THE ACQUISITION OF PROCEDURAL KNOWLEDGE THROUGH
35
PLAN INVENTION AND PLAN TRANSFORMATION
43
RECENT PROGRESS
51
LEARNING CAUSAL RELATIONS
55
KNOWLEDGE EXTRACTION THROUGH LEARNING FROM
227
LEARNING CONCEPTS WITH A PROTOTYPEBASED MODEL
233
ACQUIRING DOMAlN KNOWLEDGE FROM FRAGMENTS OF
241
CONTESTATION FOR ARGUMENTATIVE LEARNING
247
DIRECTED EXPERIMENTATION FOR THEORY REVISION AND
255
GOALFREE LEARNING BY ANALOGY
261
A SCIENTIFIC APPROACH TO PRACTICAL INDUCTION
269
EXPLORING SHIFTS OF REPRESENTATION
275

EXPLOITING FUNCTIONAL VOCABULARIES TO LEARN
71
ANALOGICAL REASONING IN THE CONTEXT OF ACQUIRING
85
A CONCEPTUAL FRAMEWORK FOR CONCEPT
99
LEARNING BY COMPOSITION
115
A SUMMARY OF CURRENT
123
ON SAFELY IGNORING HYPOTHESES
133
SYMBOLIC PROCESS
141
A METHODOLOGY FOR LEARNING INVARIANT
151
HEURISTICS AS INVARIANTS AND ITS APPLICATION TO
161
COMPONENTS OF LEARNING IN A REACTIVE ENVIRONMENT
167
THE DEVELOPMENT OF STRUCTURES THROUGH
173
HIERARCHIES AND
179
BETTER INFORMATION RETRIEVAL THROUGH LINGUISTIC
189
OVERVIEW OF THE PRODIGY LEARNING APPRENTICE
199
GENERALIZING EXPLANATIONS OF NARRATIVES INTO
207
WHY ARE DESIGN DERIVATIONS HARD TO REPLAY?
213
AN ARCHITECTURE FOR EXPERIENTIAL LEARNING
219
CURRENT RESEARCH ON LEARNING IN SOAR
281
LEARNING CONCEPTS IN A COMPLEX ROBOT WORLD
291
LEARNING FROM DATA WITH ERRORS
299
LEARNING CLASSICAL PHYSICS
307
LEARNING CONTROL INFORMATION
317
A STRATEGY FOR
327
CONCEPTUAL CLUSTERING OF STRUCTURED OBJECTS
333
ON COMPILING EXPLAlNABLE MODELS OF A DESIGN
343
WHAT CAN BE LEARNED?
349
LEARNING A DOMAIN THEORY BY COMPLETING
359
OVERVIEW OF THE ODYSSEUS LEARNING APPRENTICE
369
LEARNING FROM EXCEPTIONS IN DATABASES
375
LEARNING PHRASES IN CONTEXT
385
REFERENCES
391
INDEX
425
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