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Machine Learning Proceedings 1995 - 1st Edition - ISBN: 9781558603776, 9781483298665

Machine Learning Proceedings 1995

1st Edition

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

Editors: Armand Prieditis Stuart Russell
eBook ISBN: 9781483298665
Imprint: Morgan Kaufmann
Published Date: 1st July 1995
Page Count: 400
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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


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


No. of pages:
© Morgan Kaufmann 1995
1st July 1995
Morgan Kaufmann
eBook ISBN:

About the Editors

Armand Prieditis

Stuart Russell

Ratings and Reviews