Machine Learning: An Artificial Intelligence Approach, Band 2

Saul Amarel, Tom Michael Mitchell

Morgan Kaufmann, 1986 - 738 Seiten

Inhalt

• Challenges of: 27
• PART TWO LEARNING CONCEPTS AND RULES FROM: 43
• Learning to Predict Sequences: 64
• Shift of Bias for Inductive Concept Learning: 107
• The Effect of Noise on Concept Learning: 149
• Learning Concepts by Asking Questions: 167
• Improving the Generalization Step: 215
• PART THREE COGNITIVE ASPECTS OF LEARNING: 245
• The General: 289
• Toward: 311
• PART FOUR LEARNING BY ANALOGY: 349
• A Theory: 371
• Programming by Analogy: 393
• PART FIVE LEARNING BY OBSERVATION AND: 423
• Inventing: 471
• Program Synthesis as a Theory Formation: 499
• An Approach to Learning from Observation: 571
• PART SIX AN EXPLORATION OF GENERAL ASPECTS: 591
• Learning Control: 647
• Bibliography of Recent Machine Learning Research: 671
• Updated Glossary of Selected Terms in Machine Learning: 707
• About the Authors: 715
• Author Index: 725

Beliebte Passagen

Seite 285 - The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the Defense Advanced Research Projects Agency or the US Government.‎

Seite 285 - N0014-84K-0415 and in part by the Defense Advanced Research Projects Agency (DoD), ARPA Order No. 3597, monitored by the Air Force Avionics Laboratory under Contract F33615-81-K-1539.‎

Seite 60 - This report describes research done at the Artificial Intelligence Laboratory of the Massachusetts Institute of Technology. Support for the laboratory's artificial intelligence research is provided in part by the Advanced Research Projects Agency of the Department of Defense under Office of Naval Research contract N00014-85-K-0124.‎

Seite 10 - Learning denotes changes in the system that are adaptive in the sense that they enable the system to do the same task or tasks drawn from the same population more efficiently and more effectively the next time" (quotation from HA Simon in [7.2]).‎

Seite 371 - Derivational analogy, a method of solving problems based on the transfer of past experience to new problem situations, is discussed in the context of other general approaches to problem solving. The experience transfer process consists of recreating lines of reasoning, including decision sequences and accompanying justifications, that proved effective in solving particular problems requiring similar initial analysis. The role of derivational analogy in case-based reasoning and in automated expertise...‎

Seite 151 - Decision Tree (DT) The basic aim of any concept-learning symbolic system is to construct rules for classifying objects given a training set of objects whose class labels are known. The objects are described by a fixed collection of attributes, each with its own set of discrete values and each object belongs to one of two classes. The rules derived in our case will form a decision tree (DT). The decision tree employed is Quinlan's C4.5 [12].‎

Seite 253 - The Chunking Hypothesis: A human acquires and organizes knowledge of the environment by forming and storing expressions, called chunks, which are structured collections of the chunks existing at the time of learning. The...‎

Seite 47 - Duncan, and Macduff. Macbeth is an evil noble. Lady Macbeth is a greedy, ambitious woman. Duncan is a king. Macduff is a noble. Lady Macbeth persuades Macbeth to want to be king because she is greedy. She is able to influence him because he is married to her and because he is weak. Macbeth murders Duncan with a knife. Macbeth murders Duncan because Macbeth wants to be king and because Macbeth is evil. Lady Macbeth kills herself.‎

Bibliografische Informationen

• Titel: Machine Learning: An Artificial Intelligence Approach, Band 2; Machine Learning: An Artificial Intelligence Approach, Jaime Guillermo Carbonell
• Autoren: Saul Amarel, Tom Michael Mitchell
• Herausgeber: Ryszard Stanisław Michalski, Jaime Guillermo Carbonell, Tom Michael Mitchell
• Mitwirkende Personen: John Robert Anderson
• Ausgabe: illustriert
• Verlag: Morgan Kaufmann, 1986
• ISBN: 0934613001, 9780934613002
• Länge: 738 Seiten