Statistical Learning Theory and Stochastic Optimization Ecole d'Eté de Probabilités de Saint-Flour XXXI - 2001 /

• Κύριος συγγραφέας: Catoni, Olivier (Συγγραφέας, http://id.loc.gov/vocabulary/relators/aut)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Άλλοι συγγραφείς: Picard, Jean (Επιμελητής έκδοσης, http://id.loc.gov/vocabulary/relators/edt)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Berlin, Heidelberg : Springer Berlin Heidelberg : Imprint: Springer, 2004.
• Έκδοση: 1st ed. 2004.
• Σειρά: Lecture Notes in Mathematics, 1851
• Θέματα: Probabilities.; Statistics .; Mathematical optimization.; Artificial intelligence.; Information theory.; Numerical analysis.; Probability Theory and Stochastic Processes.; Statistical Theory and Methods.; Optimization.; Artificial Intelligence.; Information and Communication, Circuits.; Numerical Analysis.

Statistical learning theory is aimed at analyzing complex data with necessarily approximate models. This book is intended for an audience with a graduate background in probability theory and statistics. It will be useful to any reader wondering why it may be a good idea, to use as is often done in p...

Πίνακας περιεχομένων:

• Universal Lossless Data Compression
• Links Between Data Compression and Statistical Estimation
• Non Cumulated Mean Risk
• Gibbs Estimators
• Randomized Estimators and Empirical Complexity
• Deviation Inequalities
• Markov Chains with Exponential Transitions
• References
• Index.