Machine Learning Proceedings 1995

Machine Learning Proceedings 1995

Proceedings of the Twelfth International Conference on Machine Learning, Tahoe City, California, July 9-12 1995

1st Edition - July 1, 1995

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  • Editors: Armand Prieditis, Stuart Russell
  • eBook ISBN: 9781483298665

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Machine Learning: Proceedings of the Twelfth International Conference on Machine Learning covers the papers presented at the Twelfth International Conference on Machine Learning (ML95), held at the Granlibakken Resort in Tahoe City, California on July 9-12, 1995. The book focuses on the processes, methodologies, principles, and approaches involved in machine learning, including inductive logic programming algorithms, neural networks, and decision trees. The selection first offers information on the theory and applications of agnostic PAC-learning with small decision trees; reinforcement learning with function approximation; and inductive learning of reactive action models. Discussions focus on inductive logic programming algorithm, collecting instances for learning, residual gradient algorithms, direct algorithms, and learning curves for decision trees of small depth. The text then elaborates on visualizing high-dimensional structure with the incremental grid growing neural network; empirical support for winnow and weighted-majority based algorithms; and automatic selection of split criterion during tree growing based on node location. The manuscript takes a look at learning hierarchies from ambiguous natural language data, learning with rare cases and small disjuncts, learning by observation and practice, and learning collection fusion strategies for information retrieval. The selection is a valuable source of data for mathematicians and researchers interested in machine learning.

Table of Contents

  • Preface

    Advisory Committee

    Program Committee

    Auxiliary Reviewers




    Contributed Papers

    On-Line Learning of Binary Lexical Relations Using Two-Dimensional Weighted Majority Algorithms

    On Handling Tree-Structured Attributes in Decision Tree Learning

    Theory and Applications of Agnostic PAC-Learning with Small Decision Trees

    Residual Algorithms: Reinforcement Learning with Function Approximation

    Removing the Genetics from the Standard Genetic Algorithm

    Inductive Learning of Reactive Action Models

    Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network

    Empirical Support for Winnow and Weighted-Majority Based Algorithms: Results on a Calendar Scheduling Domain

    Automatic Selection of Split Criterion During Tree Growing Based on Node Location

    A Lexically Based Semantic Bias for Theory Revision

    A Comparative Evaluation of Voting and Meta-Learning on Partitioned Data

    Fast and Efficient Reinforcement Learning with Truncated Temporal Differences

    K*: An Instance-Based Learner Using an Entropic Distance Measure

    Fast Effective Rule Induction

    Text Categorization and Relational Learning

    Protein Folding: Symbolic Refinement Competes with Neural Networks

    A Bayesian Analysis of Algorithms for Learning Finite Functions

    Committee-Based Sampling For Training Probabilistic Classifiers

    Learning Prototypical Concept Descriptions

    A Case Study of Explanation-Based Control

    Explanation-Based Learning and Reinforcement Learning: A Unified View

    Lessons from Theory Revision Applied to Constructive Induction

    Supervised and Unsupervised Discretization of Continuous Features

    Bounds on the Classification Error of the Nearest Neighbor Rule

    Q-Learning for Bandit Problems

    Distilling Reliable Information From Unreliable Theories

    A Quantitative Study of Hypothesis Selection

    Learning Proof Heuristics by Adapting Parameters

    Efficient Algorithms for Finding Multi-Way Splits for Decision Trees

    Ant-Q: A Reinforcement Learning Approach to the Traveling Salesman Problem

    Stable Function Approximation in Dynamic Programming

    The Challenge of Revising an Impure Theory

    Symbiosis in Multimodal Concept Learning

    Tracking the Best Expert

    Reinforcement Learning by Stochastic Hill Climbing on Discounted Reward

    Automatic Parameter Selection by Minimizing Estimated Error

    Error-Correcting Output Coding Corrects Bias and Variance

    Learning to Make Rent-to-Buy Decisions with Systems Applications

    NewsWeeder: Learning to Filter Netnews

    Hill Climbing Beats Genetic Search on a Boolean Circuit Synthesis Problem of Koza's

    Case-Based Acquisition of Place Knowledge

    Comparing Several Linear-Threshold Learning Algorithms on Tasks Involving Superfluous Attributes

    Learning Policies for Partially Observable Environments: Scaling Up

    Increasing the Performance and Consistency of Classification Trees by Using the Accuracy Criterion at the Leaves

    Efficient Learning with Virtual Threshold Gates

    Instance-Based Utile Distinctions for Reinforcement Learning with Hidden State

    Efficient Learning from Delayed Rewards through Symbiotic Evolution

    Free to Choose: Investigating the Sample Complexity of Active Learning of Real Valued Functions

    On Learning Decision Committees

    Inferring Reduced Ordered Decision Graphs of Minimum Description Length

    On Pruning and Averaging Decision Trees

    Efficient Memory-Based Dynamic Programming

    Using Multidimensional Projection to Find Relations

    Compression-Based Discretization of Continuous Attributes

    MDL and Categorical Theories (Continued)

    For Every Generalization Action, Is There Really an Equal and Opposite Reaction? Analysis of the Conservation Law for Generalization Performance

    Active Exploration and Learning in Real-Valued Spaces Using Multi-Armed Bandit Allocation Indices

    Discovering Solutions with Low Kolmogorov Complexity and High Generalization Capability

    A Comparison of Induction Algorithms for Selective and Non-Selective Bayesian Classifiers

    Retrofitting Decision Tree Classifiers Using Kernel Density Estimation

    Automatic Speaker Recognition: An Application of Machine Learning

    An Inductive Learning Approach to Prognostic Prediction

    TD Models: Modeling the World at a Mixture of Time Scales

    Learning Collection Fusion Strategies for Information Retrieval

    Learning by Observation and Practice: An Incremental Approach for Planning Operator Acquisition

    Learning with Rare Cases and Small Disjuncts

    Horizontal Generalization

    Learning Hierarchies from Ambiguous Natural Language Data

    Invited Talks (Abstracts Only)

    Machine Learning and Information Retrieval

    Learning With Bayesian Networks

    Learning for Automotive Collision Avoidance and Autonomous Control

    Author Index

Product details

  • No. of pages: 400
  • Language: English
  • Copyright: © Morgan Kaufmann 1995
  • Published: July 1, 1995
  • Imprint: Morgan Kaufmann
  • eBook ISBN: 9781483298665

About the Editors

Armand Prieditis

Stuart Russell

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