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Machine Learning

Overview of attention for book
Cover of 'Machine Learning'

Table of Contents

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    Book Overview
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    Chapter 1 Judge: A Case-based Reasoning System
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    Chapter 2 Changing Language while Learning Recursive Descriptions from Examples
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    Chapter 3 Learning by Disjunctive Spanning
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    Chapter 4 Transfer of Knowledge Between Teaching and Learning Systems
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    Chapter 5 Some Approaches to Knowledge Acquisition
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    Chapter 6 Analogical Learning with Multiple Models
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    Chapter 7 The World Modelers Project: Objectives and Simulator Architecture
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    Chapter 8 The Acquisition of Procedural Knowledge through Inductive Learning
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    Chapter 9 Learning Static Evaluation Functions by Linear Regression
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    Chapter 10 Plan Invention and Plan Transformation
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    Chapter 11 A Brief Overview of Explanatory Schema Acquisition
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    Chapter 12 The EG Project: Recent Progress
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    Chapter 13 Learning Causal Relations
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    Chapter 14 Functional Properties and Concept Formation
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    Chapter 15 Explanation-Based Learning in Logic Circuit Design
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    Chapter 16 A Proposed Method of Conceptual Clustering for Structured and Decomposable Objects
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    Chapter 17 Exploiting Functional Vocabularies to Learn Structural Descriptions
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    Chapter 18 Combining Numeric and Symbolic Learning Techniques
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    Chapter 19 Learning by Understanding Analogies
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    Chapter 20 Analogical Reasoning in the Context of Acquiring Problem Solving Expertise
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    Chapter 21 Planning and Learning in a Design Domain: The Problems Plan Interactions
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    Chapter 22 Inference of Incorrect Operators
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    Chapter 23 A Conceptual Framework for Concept Identification
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    Chapter 24 Neural Modeling as one Approach to Machine Learning
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    Chapter 25 Steps Toward Building a Dynamic Memory
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    Chapter 26 Learning by Composition
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    Chapter 27 Knowledge Acquisition: Investigations and General Principles
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    Chapter 28 Purpose-Directed Analogy: A Summary of Current Research
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    Chapter 29 Development of a Framework for Contextual Concept Learning
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    Chapter 30 On Safely Ignoring Hypotheses
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    Chapter 31 A Model of Acquiring Problem Solving Expertise
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    Chapter 32 Another Learning Problem: Symbolic Process Prediction
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    Chapter 33 Learning at LRI Orsay
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    Chapter 34 Coper: A Methodology for Learning Invariant Functional Descriptions
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    Chapter 35 Using Experience as a Guide for Problem Solving
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    Chapter 36 Heuristics as Invariants and its Application to Learning
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    Chapter 37 Components of Learning in a Reactive Environment
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    Chapter 38 The Development of Structures Through Interaction
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    Chapter 39 Complex Learning Environments: Hierarchies and the Use of Explanation
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    Chapter 40 Prediction and Control in an Active Environment
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    Chapter 41 Better Information Retrieval Through Linguistic Sophistication
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    Chapter 42 Machine Learning Research in the Artificial Intelligence Laboratory at Illinois
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    Chapter 43 Overview of the Prodigy Learning Apprentice
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    Chapter 44 A Learning Apprentice System for VLSI Design
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    Chapter 45 Generalizing Explanations of Narratives into Schemata
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    Chapter 46 Why are Design Derivations Hard to Replay?
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    Chapter 47 An Architecture for Experiential Learning
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    Chapter 48 Knowledge Extraction Through Learning from Examples
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    Chapter 49 Learning Concepts with a Prototype-Based Model for Concept Representation
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    Chapter 50 Recent Progress on the Mathematician’s Apprentice Project
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    Chapter 51 Acquiring Domain Knowledge from Fragments of Advice
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    Chapter 52 Calm: Contestation for Argumentative Learning Machine
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    Chapter 53 Directed Experimentation for Theory Revision and Conceptual Knowledge Acquisition
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    Chapter 54 Goal-free Learning by Analogy
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    Chapter 55 A Scientific Approach To Practical Induction
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    Chapter 56 Exploring Shifts of Representation
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    Chapter 57 Current Research on Learning in Soar
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    Chapter 58 Learning Concepts in a Complex Robot World
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    Chapter 59 Learning Evaluation Functions
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    Chapter 60 Learning from Data with Errors
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    Chapter 61 Explanation-Based Manipulator Learning
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    Chapter 62 Learning Classical Physics
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    Chapter 63 Views and Causality in Discovery: Modelling Human Induction
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    Chapter 64 Learning Control Information
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    Chapter 65 An Investigation of the Nature of Mathematical Discovery
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    Chapter 66 Learning How to Reach a Goal: A Strategy for the Multiple Classes Classification Problem
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    Chapter 67 Conceptual Clustering of Structured Objects
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    Chapter 68 Learning in Intractable Domains
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    Chapter 69 On Compiling Explainable Models of a Design Domain
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    Chapter 70 What can be Learned?
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    Chapter 71 Learning Heuristic Rules from Deep Reasoning
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    Chapter 72 Learning a Domain Theory by Completing Explanations
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    Chapter 73 Learning Implementation Rules with Operating-Conditions Depending on Internal Structures in VLSI Design
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    Chapter 74 Overview of the Odysseus Learning Apprentice
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    Chapter 75 Learning from Exceptions in Databases
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    Chapter 76 Learning Apprentice Systems Research at Schlumberger
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    Chapter 77 Language Acquisition: Learning Phrases in Context
Overall attention for this book and its chapters
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Title
Machine Learning
Published by
Springer, Boston, MA, January 1986
DOI 10.1007/978-1-4613-2279-5
ISBNs
978-1-4612-9406-1, 978-1-4613-2279-5
Authors

Tom M. Mitchell, Jaime G. Carbonell, Ryszard S. Michalski, Mitchell, Tom M., Carbonell, Jaime G., Michalski, Ryszard S.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 1 50%
Unknown 1 50%
Readers by discipline Count As %
Computer Science 1 50%
Unknown 1 50%