Machine Learning: ECML-94: European Conference on Machine Learning, Catania, Italy, April 6-8, 1994. Proceedings / Edition 1

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Overview

This volume contains the proceedings of the European Conference on Machine Learning 1994, which continues the tradition of earlier meetings and which is a major forum for the presentation of the latest and most significant results in machine learning.
Machine learning is one of the most important subfields of artificial intelligence and computer science, as it is concerned with the automation of learning processes.
This volume contains two invited papers, 19 regular papers, and 25 short papers carefully reviewed and selected from in total 88 submissions.
The papers describe techniques, algorithms, implementations, and experiments in the area of machine learning.

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Product Details

Table of Contents

Industrial Applications of ML: Illustrations for the KAML Dilemma and the CBR Dream 3
Knowledge Representation in Machine Learning 20
Inverting Implication with Small Training Sets 31
A Context Similarity Measure 49
Incremental Learning of Control Knowledge for Nonlinear Problem Solving 64
Characterizing the Applicability of Classification Algorithms Using Meta-Level Learning 83
Inductive Learning of Characteristic Concept Descriptions from Small Sets of Classified Examples 103
FOSSIL : A Robust Relational Learner 122
A Multistrategy Learning System and Its Integration into an Interactive Floorplanning Tool 138
Bottom-Up Induction of Oblivious Read-Once Decision Graphs 154
Estimating Attributes: Analysis and Extensions of RELIEF 171
BMWk Revisited: Generalisation and Formalization of an Algorithm for Detecting Recursive Relations in Term Sequences 183
An Analytic and Empirical Comparison of Two Methods for Discovering Probabilistic Causal Relationships 198
Sample PAC-Learnability in Model Inference 217
Averaging Over Decision Stumps 231
Controlling Constructive Induction in CIPF: An MDL Approach 242
Using Constraints to Building Version Spaces 257
On the Utility of Predicate Invention in Inductive Logic Programming 272
Learning Problem-Solving Concepts by Reflecting on Problem Solving 287
Existence and Nonexistence of Complete Refinement Operators 307
A Hybrid Nearest-Neighbor and Nearest-Hyperrectangle Algorithm 323
Automated Knowledge Acquisition for PROSPECTOR-like Expert Systems 339
On the Role of Machine Learning in Knowledge-Based Control 343
Discovering Dynamics with Genetic Programming 347
A Geometric Approach to Feature Selection 351
Identifying Unrecognizable Regular Languages by Queries 355
Intensional Learning of Logic Programs 359
Partially Isomorphic Generalization and Analogical Reasoning 363
Learning from Recursive, Tree Structured Examples 367
Concept Formation in Complex Domains 371
An Algorithm for Learning Hierarchical Classifiers 375
Learning Belief Network Structure from Data Under Causal Insufficiency 379
Cost-Sensitive Pruning of Decision Trees 383
An Instance-Based Learning Method for Databases: An Information Theoretic Approach 387
Early Screening for Gastric Cancer Using Machine Learning Techniques 391
DP1: Supervised and Unsupervised Clustering 395
Using Machine Learning Techniques to Interpret Results from Discrete Event Simulation 399
Flexible Integration of Multiple Learning Methods into a Problem Solving Architecture 403
Concept Sublattices 407
The Piecewise Linear Classifier DIPOL92 411
Complexity of Computing Generalized VC-Dimensions 415
Learning Relations Without Closing the World 419
Properties of Inductive Logic Programming in Function-Free Horn Logic 423
Representing Biases for Inductive Logic Programming 427
Biases and Their Effects in Inductive Logic Programming 431
Inductive Learning of Normal Clauses 435
Author Index 439
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