《机器学习 第2卷 上下》

PART ONE GENERAL ISSUES1

Chapter 1 Understanding the Nature of Learning:Issues and Research Directions3

CONTENTSPreface9

Ryszard S.MichalskiChapter 2 Machine Learning:Challenges of theEighties27

Ryszard S.Michalski,Saul Amarel,Douglas B.Lenat,Donald Michie,andPatrick H.Winston(Edited by Gail Thornburg and Ryszard Michalski)PART TWO LEARNING CONCEPTS AND RULES FROMEXAMPLES43

Chapter 3 Learning by Augmenting Rules andAccumulating Censors　45

Patrick H.WinstonChapter 4 Learning to Predict Sequences63

Thomas G.Dietterich and Ryszard S.MichalskiChapter 5 Shift of Bias for Inductive Concept Learning107

Paul E. UtgoffChapter 6 The Effect of Noise on Concept Learning149

J.Ross QUinlanChapter 7 Learning Concepts by Asking Questions167

Claude Sammut and Ranan B.BanerjiChapter 8 Concept Learning in a Rich Input Domain:Generalization-Based Memory193

Michael LebowitzChapter 9 Improving the Generalization Step inLearn ing215

Yves Kodra toff and Jean-Gabriel GanasciaPART THREE COGNITIVE ASPECTS 0F LEARNING245

Chapter 10 The Chunking of Goal Hierarchies:AGeneralized Model of Practice247

Paul S.Rosenbloom and Allen NewellChapter 11 Knowledge Compilation:The GeneralLearning Mechanism289

John R.AndersonChapter 12 Learning Physical Domains:Toward aTheoreticel Framework311

Kenneth D.Forbus and Dedre GentnerPART FOUR LEARNING BY ANALOGY349

Chapter 13 Concept Formation by IncrementalAnalogical Reasoning and Debugging351

Mark H.BursteinChapter 14 Derivational Analogy:A Theory ofReconstructive Problem Solving andExpertise Acquisition371

JaimeG.CarbonellPART FIVE LEARNING BY OBSERVATION ANDDISCOVERY393

Chapter 15 Programming by Analogy395

Nachum DershowitzChapter 16 The Search for Regularity:Four Aspects ofScientific Discovery　425

Pat Langley,Jan M.Zytkow,Herbert A.Simon,and Gary L.BradshawChapter 17 Conceptual Clustering:InventingGoal-Oriented Classifications of StructuredObjects　471

Robert E.Stepp Ⅲ and Ryszard S.MichalskiChapter 18 Program Synthesis as a Theory FormationTask:Problem Representations and SolutionMethods　499

Saul AmarelChapter 19 An Approach to Learning from Observation571

Gerald DeJongPART SIX AN EXPLORATION OF GENERAL ASPECTS591

OF LEARNINGChapter 20 Escaping Brittleness:The Possibilities ofGeneral-Purpose Learning AlgorithmsApplied to Parallel Rule-Based Systems593

John H.HollandChapter 21 Learning from Positive-Only Examples:TheSubset Principle and Three Case Studies625

Robert C.BerwickChapter 22 Precondition Analysis:Learning ControlInformation647

Bernard SilverBibliography of Recent Machine Learning Research　671

Smadar T.Kedar-Cabelli and Sridhar MahadevanUpdated Glossary of Selected Terms In Machine Learning707

About the Authors715

Author Index725

Subject Index729