This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias.
Le informazioni nella sezione "Riassunto" possono far riferimento a edizioni diverse di questo titolo.
1 Introduction.- 1.1 Machine Learning.- 1.2 Learning Concepts from Examples.- 1.3 Role of Bias in Concept Learning.- 1.4 Kinds of Bias.- 1.5 Origin of Bias.- 1.6 Learning to Learn.- 1.7 The New-Term Problem.- 1.8 Guide to Remaining Chapters.- 2 Related Work.- 2.1 Learning Programs that use a Static Bias.- 2.1.1 Vere’s Thoth without Counterfactuals.- 2.1.2 Vere’s Thoth with Counterfactuals.- 2.1.3 Mitchell’s Candidate Elimination.- 2.1.4 Michalski’s STAR Algorithm.- 2.2 Learning Programs that use a Dynamic Bias.- 2.2.1 Waterman’s Poker Player.- 2.2.2 Lenat’s EURISKO.- 3 Searching for a Better Bias.- 3.1 Simplifications.- 3.1.1 Original Bias.- 3.1.2 Representation of Bias.- 3.1.3 Formalism for Description Language.- 3.1.4 Strength of Bias.- 3.1.5 When to Shift to a Weaker Bias.- 3.2 The RTA Method for Shifting Bias.- 3.2.1 Recommending New Descriptions for a Weaker Bias.- 3.2.2 Translating Recommendations into New Concept Descriptions.- 3.2.3 Assimilating New Concepts into the Hypothesis Space.- 4 LEX and STABB.- 4.1 LEX: A Program that Learns from Experimentation.- 4.1.1 Problem Solver.- 4.1.2 Critic.- 4.1.3 Generalizer.- 4.1.4 Problem Generator.- 4.1.5 Description Language.- 4.1.6 Matching Two Descriptions.- 4.1.7 Operator Language.- 4.2 STABB: a Program that Shifts Bias.- 5 Least Disjunction.- 5.1 Procedure.- 5.1.1 Recommend.- 5.1.2 Translate.- 5.1.3 Assimilate.- 5.2 Requirements.- 5.3 Experiments.- 5.3.1 Experiment #1.- 5.3.2 Experiment #2.- 5.4 Example Trace.- 5.5 Discussion.- 5.5.1 Language Shift and Version Spaces.- 5.5.2 Obsolete Descriptions: Strengthening Bias.- 5.5.3 Choosing Among Syntactic Methods.- 6 Constraint Back-Propagation.- 6.1 Procedure.- 6.1.1 Recommend.- 6.1.2 Translate.- 6.1.3 Assimilate.- 6.2 Requirements.- 6.3 Experiments.- 6.3.1 Experiment #1.- 6.3.2 Experiment #2.- 6.3.3 Experiment #3.- 6.4 Example Trace.- 6.5 Discussion.- 6.5.1 Knowledge Based Assimilation.- 6.5.2 Knowledge Based Set Equivalence.- 6.5.3 Bias in Formalism of Description Language.- 6.5.4 Interaction of Operator Language and Description Language.- 6.5.5 A Method for Computing a Strong and Correct Bias.- 6.5.6 Regressing Sub-Goals.- 7 Conclusion.- 7.1 Summary.- 7.2 Results.- 7.3 Issues.- 7.3.1 Role of Bias.- 7.3.2 Sources of Bias.- 7.3.3 When to Shift.- 7.3.4 Strength of Bias.- 7.3.5 How to Shift Bias.- 7.3.6 Recommending New Descriptions.- 7.3.7 Translating Recommendations.- 7.3.8 Assimilating New Descriptions.- 7.3.9 Side Effects.- 7.3.10 Multiple Uses of Concept Description Language.- 7.4 Further Work.- Appendix A: Lisp Code.- A.1 STABB.- A.2 Grammar.- A.3 Intersection.- A.4 Match.- A.5 Operators.- A.6 Utilities.
Book by Utgoff Paul E
Le informazioni nella sezione "Su questo libro" possono far riferimento a edizioni diverse di questo titolo.
GRATIS per la spedizione in U.S.A.
Destinazione, tempi e costiEUR 3,63 per la spedizione in U.S.A.
Destinazione, tempi e costiDa: Better World Books, Mishawaka, IN, U.S.A.
Condizione: Very Good. 1986th Edition. Former library book; may include library markings. Used book that is in excellent condition. May show signs of wear or have minor defects. Codice articolo 53532517-6
Quantità: 1 disponibili
Da: Ammareal, Morangis, Francia
Hardcover. Condizione: Très bon. Ancien livre de bibliothèque. Légères traces d'usure sur la couverture. Couverture différente. Edition 1986. Ammareal reverse jusqu'à 15% du prix net de cet article à des organisations caritatives. ENGLISH DESCRIPTION Book Condition: Used, Very good. Former library book. Slight signs of wear on the cover. Different cover. Edition 1986. Ammareal gives back up to 15% of this item's net price to charity organizations. Codice articolo E-598-140
Quantità: 1 disponibili
Da: Michael Knight, Bookseller, Forest Grove, OR, U.S.A.
hardcover. Condizione: Very Good. Hardcover issued without dust-jacket. Clean and solid. Ships from a smoke-free home. Codice articolo mon0000013502
Quantità: 1 disponibili
Da: GoldBooks, Denver, CO, U.S.A.
Hardcover. Condizione: new. New Copy. Customer Service Guaranteed. Codice articolo 25Q75_31_0898382238
Quantità: 1 disponibili
Da: GoldBooks, Denver, CO, U.S.A.
Hardcover. Condizione: new. New Copy. Customer Service Guaranteed. Codice articolo 77P47_42_0898382238
Quantità: 1 disponibili
Da: Lucky's Textbooks, Dallas, TX, U.S.A.
Condizione: New. Codice articolo ABLIING23Mar2317530032024
Quantità: Più di 20 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: New. Codice articolo 2073551-n
Quantità: 15 disponibili
Da: Grand Eagle Retail, Mason, OH, U.S.A.
Hardcover. Condizione: new. Hardcover. This book is based on the author's Ph.D. dissertation[56]. The the sis research was conducted while the author was a graduate student in the Department of Computer Science at Rutgers University. The book was pre pared at the University of Massachusetts at Amherst where the author is currently an Assistant Professor in the Department of Computer and Infor mation Science. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Selection of a concept represents an inductive leap because the concept then indicates the classification of instances that have not yet been observed by the learning program. Learning programs that make undesir able inductive leaps do so due to undesirable bias. The research problem addressed here is to show how a learning program can learn a desirable inductive bias. Programs that learn concepts from examples are guided not only by the examples (and counterexamples) that they observe, but also by bias that determines which concept is to be considered as following best from the ob servations. Shipping may be from multiple locations in the US or from the UK, depending on stock availability. Codice articolo 9780898382235
Quantità: 1 disponibili
Da: GreatBookPrices, Columbia, MD, U.S.A.
Condizione: As New. Unread book in perfect condition. Codice articolo 2073551
Quantità: 15 disponibili
Da: Ria Christie Collections, Uxbridge, Regno Unito
Condizione: New. In. Codice articolo ria9780898382235_new
Quantità: Più di 20 disponibili