Cargando…
Machine learning : a theoretical approach /
This is the first comprehensive introduction to computational learning theory. The author's uniform presentation of fundamental results and their applications offers AI researchers a theoretical perspective on the problems they study. The book presents tools for the analysis of probabilistic mo...
| Clasificación: | Libro Electrónico |
|---|---|
| Autor principal: | |
| Formato: | Electrónico eBook |
| Idioma: | Inglés |
| Publicado: |
San Mateo, CA :
M. Kaufmann,
�1991.
|
| Temas: | |
| Acceso en línea: | Texto completo |
Tabla de Contenidos:
- Front Cover; Copyright Page; Machine Learning: A Theoretical Approach; Table of Contents; Chapter 1. Introduction; 1.1 Bibliographic Notes; Chapter 2. Learning Concepts on Countable Domains; 2.1 Preliminaries; 2.2 Sample Complexity; 2.3 Dimension and Learnability; 2.4 Learning Concepts with One-Sided Error; 2.5 Summary; 2.6 Appendix; 2.7 Exercises; 2.8 Bibliographic Notes; Chapter 3. Time Complexity of Concept Learning; 3.1 Preliminaries; 3.2 Polynomial-Time Learnability; 3.3 Occam's Razor; 3.4 One-Sided Error; 3.5 Hardness Results; 3.6 Summary; 3.7 Appendix; 3.8 Exercises.
- 3.9 Bibliographic NotesChapter 4. Learning Concepts on Uncountable Domains; 4.1 Preliminaries; 4.2 Uniform Convergence and Learnability; 4.3 Summary; 4.4 Appendix; 4.5 Exercises; 4.6 Bibliographic Notes; Chapter 5. Learning Functions; 5.1 Learning Functions on Countable Domains; 5.2 Learning Functions on Uncountable Domains; 5.3 Summary; 5.4 Exercises; 5.5 Bibliographic Notes; Chapter 6. Finite Automata; 6.1 Preliminaries; 6.2 A Modified Framework.; 6.3 Summary; 6.4 Exercises; 6.5 Bibliographic Notes; Chapter 7. Neural Networks; 7.1 Preliminaries; 7.2 Bounded-Precision Networks.
- 7.3 Efficiency Issues7.4 Summary; 7.5 Appendix; 7.6 Exercises; 7.7 Bibliographie Notes; Chapter 8. Generalizing the Learning Model; 8.1 Preliminaries; 8.2 Sample Complexity; 8.3 Time Complexity; 8.4 Prediction; 8.5 Boosting; 8.6 Summary; 8.7 Exercises; 8.8 Bibliographic Notes; Chapter 9. Conclusion; 9.1 The Paradigm; 9.2 Recent and Future Directions; 9.3 An AI Perspective; Index.