Front Cover; Machine Learning; Copyright Page; Table of Contents; Preface; Advisory Committee; Program Committee; Auxiliary Reviewers; Workshops; Tutorials; PART 1: CONTRIBUTED PAPERS; Chapter 1. On-line Learning of Binary Lexical Relations Using Two-dimensional Weighted Majority Algorithms; ABSTRACT; 1 Introduction; 2 On-line Learning Model for Binary Relations; 3 Two-dimensional Weighted Majority Prediction Algorithms; 4 Experimental Results; 5 Theoretical Performance Analysis; 6 Concluding Remarks; Acknowledgement; References

Chapter 2. On Handling Tree-Structured Attributes in Decision Tree LearningAbstract; 1 Introduction; 2 Decision Trees With Tree-Structured Attributes; 3 Pre-processing Approaches; 4 A Direct Approach; 5 Analytical Comparison; 6 Experimental Comparison; 7 Summary and Conclusion; Acknowledgement; References; Chapter 3. Theory and Applications of Agnostic PAC-Learning with Small Decision Trees; Abstract; 1 INTRODUCTION; 2 THE AGNOSTIC PAC-LEARNING ALGORITHM T2; 3 EVALUATION OF T2 ON ""REAL-WORLD"" CLASSIFICATION PROBLEMS; 4 LEARNING CURVES FOR DECISION TREES OF SMALL DEPTH; 5 CONCLUSION

AcknowledgementReferences; Chapter 4. Residual Algorithms: Reinforcement Learning with Function Approximation; ABSTRACT; 1 INTRODUCTION; 2 ALGORITHMS FOR LOOKUP TABLES; 3 DIRECT ALGORITHMS; 4 RESIDUAL GRADIENT ALGORITHMS; 5 RESIDUAL ALGORITHMS; 6 STOCHASTIC MDPS AND MODELS; 7 MDPS WITH MULTIPLE ACTIONS; 8 RESIDUAL ALGORITHM SUMMARY; 9 SIMULATION RESULTS; 10 CONCLUSIONS; Acknowledgments; References; Chapter 5. Removing the Genetics from the Standard Genetic Algorithm; Abstract; 1. THE GENETIC ALGORITHM (GA); 2. FOUR PEAKS: A PROBLEM DESIGNED TO BE GA-FRIENDLY; 3. SELECTING THE GA'S PARAMETERS

4. POPULATION-BASED INCREMENTAL LEARNING5. EMPIRICAL ANALYSIS ON THE FOUR PEAKS PROBLEM; 6. DISCUSSION; 7. CONCLUSIONS; ACKNOWLEDGEMENTS; REFERENCES; Chapter 6. Inductive Learning of Reactive Action Models; Abstract; 1 INTRODUCTION; 2 CONTEXT OF THE LEARNER; 3 ACTIONS AND TELEO-OPERATORS; 4 COLLECTING INSTANCES FOR LEARNING; 5 THE INDUCTIVE LOGIC PROGRAMMING ALGORITHM; 6 EVALUATION; 7 RELATED WORK; 8 FUTURE WORK; Acknowledgements; References; Chapter 7. Visualizing High-Dimensional Structure with the Incremental Grid Growing Neural Network; Abstract; 1 INTRODUCTION; 2 INCREMENTAL GRID GROWING

3 COMPARISON USING MINIMUM SPANNING TREEDATA4 DEMONSTRATION USING REALWORLD SEMANTIC DATA; 5 DISCUSSION AND FUTURE WORK; 6 CONCLUSION; References; Chapter 8. Empirical support for Winnow and Weighted-Majority based algorithms: results on a calendar scheduling domain; Abstract; 1 Introduction; 2 The learning problem; 3 Description of the algorithms; 4 Experimental results; 5 Theoretical results; Acknowledgements; References; Appendix; Chapter 9. Automatic Selection of Split Criterion during Tree Growing Based on Node Location; Abstract; 1 DECISION TREE CONSTRUCTION