Algorithmic learning in a random world
"Algorithmic Learning in a Random World describes recent theoretical and experimental developments in building computable approximations to Kolmogorov's algorithmic notion of randomness. Based on these approximations, a new set of machine learning algorithms have been developed that can be...
| Otros Autores: | , , |
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| Formato: | Libro |
| Idioma: | Inglés |
| Publicado: |
New York :
Springer
[2005]
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| Materias: | |
| Ver en Universidad de Navarra: | https://innopac.unav.es/record=b46292822*spi |
Tabla de Contenidos:
- Counter Preface.- List of Principal results.- Introduction.- Conformal prediction.- Classification with conformal predictors.-Modifications of conformal predictors.- Probabilistic prediction I: impossibility results.- Probabilistic prediction II: Venn predictors.- Beyond exchangeability.- On-line compression modeling I: conformal prediction.- On-line compression modeling II: Venn prediction.- Perspectives and contrasts.- Appendix A: Probability theory.- Appendix B: Data sets.- Appendix C: FAQ.- Notation.- References.- Index
