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Proceedings of the Fifth International Conference on Machine Learning provides careful theoretical analyses that make clear contact with traditional problems in machine learning. This book discusses the key role of learning in cognition.
Organized into 10 parts encompassing 49 chapters, this book begins with an overview of the OTIS induction system that learns concepts from positive and negative examples by searching through the space of possible concept descriptions. This text then reviews the methods to selecting examples, and explores the ramifications of one in detail. Other chapters consider a reported phenomenon in machine concept learning wherein concept descriptions can be simplified with little ill-effect on classification accuracy. This book discusses as well an implemented system that learns structural models of shape from noisy image data. The final chapter provides a discussion of the relationship between learning and forgetting.
This book is a valuable resource for psychologists, scientists, theorists, and research workers.
Using a Generalization Hierarchy to Learn from Examples
Tuning Rule-Based Systems to their Environments
On Asking the Right Questions
Concept Simplification and Prediction Accuracy
Learning Graph Models of Shape
Learning Categorical Decision Criteria in Biomedical Domains
Conceptual Clumping of Binary Vectors with Occam's Razor
AutoClass: A Bayesian Classification System
Incremental Multiple Concept Learning Using Experiments
Trading Off Simplicity and Coverage in Incremental Concept Learning
Deferred Commitment in UNIMEM: Waiting to Learn
Experiments on the Costs and Benefits of Windowing in ID3
Improved Decision Trees: A Generalized Version of ID3
ID5: An Incremental ID3
Using Weighted Networks to Represent Classification Knowledge in Noisy Domains
An Empirical Comparison of Genetic and Decision-Tree Classifiers
Population Size in Classifier Systems
Representation and Hidden Bias: Gray vs. Binary Coding for Genetic Algorithms
Classifier Systems with Hamming Weights
Midgard: A Genetic Approach to Adaptive Load Balancing for Distributed Systems
Some Interesting Properties of a Connectionist Inductive Learning System
Competitive Reinforcement Learning
Connectionist Learning of Expert Backgammon Evaluations
Building and Using Mental Models in a Sensory-Motor Domain: A Connectionist Approach
Reasoning about Operationality for Explanation-Based Learning
Boundaries of Operationality
On the Tractability of Learning from Incomplete Theories
Active Explanation Reduction: An Approach to the Multiple Explanations Problem
Generalizing Number and Learning from Multiple Examples in Explanation Based Learning
Generalizing the Order of Operators in Macro-Operators
Using Experience-Based Learning in Game Playing
Integrated Explanation-Based and Empirical Learning
Integrated Learning with Incorrect and Incomplete Theories
An Approach Based on Integrated Learning to Generating Stories from Stories
A Knowledge Intensive Approach to Concept Induction
Learning to Program by Examining and Modifying Cases
Theory Discovery and the Hypothesis Language
Machine Invention of First Order Predicates by Inverting Resolution
The Interdependencies of Theory Formation, Revision, and Experimentation
A Hill-Climbing Approach to Machine Discovery
Reduction: A Practical Mechanism of Searching for Regularity in Data
Formal Models of Concept Learning
Extending the Valiant Learning Model
Learning Systems of First-Order Rules
Two New Frameworks for Learning
Hypothesis Filtering: A Practical Approach to Reliable Learning
Experimental Results in Machine Learning
Diffy-S: Learning Robot Operator Schemata from Examples
Experimental Results from an Evaluation of Algorithms that Learn to Control Dynamic Systems
Utilizing Experience for Improving the Tactical Manager
Computational Impact of Learning and Forgetting
Some Chunks Are Expensive
The Role of Forgetting in Learning
- No. of pages:
- © Morgan Kaufmann 1988
- 25th December 1988
- Morgan Kaufmann
- eBook ISBN:
University of Michigan, Ann Arbor, U.S.A.
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