Machine Learning: A Guide to Current ResearchTom 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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... STRUCTURES IN VLSI DESIGN Masanobu Watanabe OVERVIEW OF THE ODYSSEUS LEARNING APPRENTICE David C. Wilkins , William J. Clancey , and Bruce G. Buchanan 369 LEARNING FROM EXCEPTIONS IN DATABASES 375 Keith E. Williamson LEARNING APPRENTICE ...
... STRUCTURES IN VLSI DESIGN Masanobu Watanabe OVERVIEW OF THE ODYSSEUS LEARNING APPRENTICE David C. Wilkins , William J. Clancey , and Bruce G. Buchanan 369 LEARNING FROM EXCEPTIONS IN DATABASES 375 Keith E. Williamson LEARNING APPRENTICE ...
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Saturs
CHANGING LANGUAGE WHILE LEARNING RECURSIVE | 5 |
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 |
GENERALIZING | 207 |
WHY ARE DESIGN DERIVATIONS HARD TO REPLAY? | 213 |
AN ARCHITECTURE FOR EXPERIENTIAL LEARNING | 219 |
KNOWLEDGE EXTRACTION THROUGH LEARNING FROM | 227 |
LEARNING CONCEPTS WITH A PROTOTYPEBASED MODEL | 233 |
ACQUIRING DOMAIN KNOWLEDGE FROM FRAGMENTS OF | 241 |
CONTESTATION FOR ARGUMENTATIVE LEARNING | 247 |
DIRECTED EXPERIMENTATION FOR THEORY REVISION AND | 255 |
EXPLANATIONBASED LEARNING IN LOGIC CIRCUIT DESIGN | 63 |
EXPLOITING FUNCTIONAL VOCABULARIES ΤΟ LEARN | 71 |
LEARNING BY UNDERSTANDING ANALOGIES | 81 |
THE | 89 |
STEPS TOWARD BUILDING A DYNAMIC MEMORY | 109 |
LEARNING BY COMPOSITION | 115 |
A SUMMARY OF CURRENT | 123 |
ON SAFELY IGNORING HYPOTHESES | 133 |
ANOTHER LEARNING | 141 |
A METHODOLOGY | 151 |
HEURISTICS AS INVARIANTS AND ITS APPLICATION TO | 161 |
COMPONENTS OF LEARNING IN A REACTIVE ENVIRONMENT | 167 |
Robert W Lawler | 173 |
PREDICTION AND CONTROL IN AN ACTIVE ENVIRONMENT | 183 |
BETTER INFORMATION RETRIEVAL THROUGH LINGUISTIC | 189 |
OVERVIEW OF THE PRODIGY LEARNING APPRENTICE | 199 |
GOALFREE LEARNING BY ANALOGY | 261 |
A SCIENTIFIC APPROACH TO PRACTICAL INDUCTION | 269 |
EXPLORING SHIFTS OF REPRESENTATION | 275 |
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 |
CONCEPTUAL CLUSTERING OF STRUCTURED OBJECTS | 333 |
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 |
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Citi izdevumi - Skatīt visu
Machine Learning: A Guide to Current Research Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski Ierobežota priekšskatīšana - 2012 |
Machine Learning Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski Priekšskatījums nav pieejams - 2014 |
Machine Learning: A Guide to Current Research Tom M. Mitchell,Jaime G. Carbonell,Ryszard S. Michalski Priekšskatījums nav pieejams - 2011 |
Bieži izmantoti vārdi un frāzes
abstraction acquired action algorithm analogy analysis applied approach architecture Artificial Intelligence behavior BigTrak Carnegie-Mellon University causal characterization chunking complex components Computer Science concept learning conceptual clustering constraints construct context defined Department of Computer derivation described descriptors developed disjunctive domain knowledge domain theory environment evaluation function experience expert system explanation explanation-based learning given goal heuristic hypotheses implemented inductive inference initial input instances interactions interpretation involves Jaime Carbonell justification knowledge acquisition knowledge base knowledge-based language learner Learning Apprentice learning mechanism learning methods learning system machine learning macro-operator memory Michalski Morgan Kaufmann objects observed Odysseus Operating-Conditions operators organism particular Pat Langley performance performance improvement plan possible preconditions prediction problem solving problem space procedure refinement relations relevant representation represented result robot rules schemata sequence similar simulation solution specific strategies structure task techniques types University values
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