Machine Learning Proceedings 1994

Machine Learning Proceedings 1994

Proceedings of the Eighth International Conference

1st Edition - July 1, 1994

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  • Editor: William W. Cohen
  • eBook ISBN: 9781483298184

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Description

Machine Learning: Proceedings of the Eleventh International Conference covers the papers presented at the Eleventh International Conference on Machine Learning (ML94), held at New Brunswick, New Jersey on July 10-13, 1994. The book focuses on the processes, methodologies, and approaches involved in machine learning, including inductive logic programming, neural networks, and decision trees. The selection first offers information on learning recursive relations with randomly selected small training sets; improving accuracy of incorrect domain theories; and using sampling and queries to extract rules from trained neural networks. The text then takes a look at boosting and other machine learning algorithms; an incremental learning approach for completable planning; and learning disjunctive concepts by means of genetic algorithms. The publication ponders on rule induction for semantic query optimization; irrelevant features and the subset selection problem; and an efficient subsumption algorithm for inductive logic programming. The book also examines Bayesian inductive logic programming; a statistical approach to decision tree modeling; and an improved algorithm for incremental induction of decision trees. The selection is a dependable source of data for researchers interested in machine learning.

Table of Contents


  • Preface

    Workshops

    Tutorials

    Organizing Committee

    Program Committee

    Schedule

    Contributed Papers

    A New Method for Predicting Protein Secondary Structures Based on Stochastic Tree Grammars

    Learning Recursive Relations with Randomly Selected Small Training Sets

    Improving Accuracy of Incorrect Domain Theories

    Greedy Attribute Selection

    Using Sampling and Queries to Extract Rules from Trained Neural Networks

    The Generate, Test, and Explain Discovery System Architecture

    Boosting and Other Machine Learning Algorithms

    In Defense of C4.5: Notes on Learning One-Level Decision Trees

    Incremental Reduced Error Pruning

    An Incremental Learning Approach for Completable Planning

    Learning by Experimentation: Incremental Refinement of Incomplete Planning Domains

    Learning Disjunctive Concepts by Means of Genetic Algorithms

    Consideration of Risk in Reinforcement Learning

    Rule Induction for Semantic Query Optimization

    Irrelevant Features and the Subset Selection Problem

    An Efficient Subsumption Algorithm for Inductive Logic Programming

    Getting the Most from Flawed Theories

    Heterogeneous Uncertainty Sampling for Supervised Learning

    Markov Games as a Framework for Multi-Agent Reinforcement Learning

    To Discount or Not to Discount in Reinforcement Learning: A Case Study Comparing R Learning and Q Learning

    Comparing Methods for Refining Certainty-Factor Rule-Bases

    Reward Functions for Accelerated Learning

    Efficient Algorithms for Minimizing Cross Validation Error

    Revision of Production System Rule-Bases

    Using Genetic Search to Refine Knowledge-Based Neural Networks

    Reducing Misclassification Costs

    Incremental Multi-Step Q-Learning

    The Minimum Description Length Principle and Categorical Theories

    Towards a Better Understanding of Memory-Based Reasoning Systems

    Hierarchical Self-Organization in Genetic Programming

    A Conservation Law for Generalization Performance

    On the Worst-Case Analysis of Temporal-Difference Learning Algorithms

    A Constraint-Based Induction Algorithm in FOL

    Learning Without State-Estimation in Partially Observable Markovian Decision Processes

    Prototype and Feature Selection by Sampling and Random Mutation Hill Climbing Algorithms

    A Baysian Framework to Integrate Symbolic and Neural Learning

    A Modular Q-Learning Architecture for Manipulator Task Decomposition

    An Improved Algorithm for Incremental Induction of Decision Trees

    A Powerful Heuristic for the Discovery of Complex Patterned Behavior

    Small Sample Decision Tree Pruning

    Combining Top-Down and Bottom-Up Techniques in Inductive Logic Programming

    Selective Reformulation of Examples in Concept Learning

    Invited Talks

    A Statistical Approach to Decision Tree Modeling

    Bayesian Inductive Logic Programming

    Frequencies Vs Biases: Machine Learning Problems in Natural Language Processing—Abstract

    Author Index

Product details

  • No. of pages: 381
  • Language: English
  • Copyright: © Morgan Kaufmann 1994
  • Published: July 1, 1994
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483298184

About the Editor

William W. Cohen

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