Estimation of Dependences Based on Empirical Data (Springer Series in Statistics)

Vapnik, V.

ISBN 10: 0387907335 / ISBN 13: 9780387907338

Editore: Springer, 1982

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Dati bibliografici

• Titolo: Estimation of Dependences Based on Empirical Data (Springer Series in Statistics)
• Editore: Springer
• Data di pubblicazione: 1982
• Lingua: Inglese
• ISBN 10: 0387907335
• ISBN 13: 9780387907338
• Rilegatura: Hardcover
• Condizione: New

Descrizione articolo

Riassunto

Twenty-?ve years have passed since the publication of the Russian version of the book Estimation of Dependencies Based on Empirical Data (EDBED for short). Twen- ?ve years is a long period of time. During these years many things have happened. Looking back, one can see how rapidly life and technology have changed, and how slow and dif?cult it is to change the theoretical foundation of the technology and its philosophy. I pursued two goals writing this Afterword: to update the technical results presented in EDBED (the easy goal) and to describe a general picture of how the new ideas developed over these years (a much more dif?cult goal). The picture which I would like to present is a very personal (and therefore very biased) account of the development of one particular branch of science, Empirical - ference Science. Such accounts usually are not included in the content of technical publications. I have followed this rule in all of my previous books. But this time I would like to violate it for the following reasons. First of all, for me EDBED is the important milestone in the development of empirical inference theory and I would like to explain why. S- ond, during these years, there were a lot of discussions between supporters of the new 1 paradigm (now it is called the VC theory ) and the old one (classical statistics).

Contenuti

Realism and Instrumentalism: Classical Statistics and VC Theory (1960–1980).- Falsifiability and Parsimony: VC Dimension and the Number of Entities (1980–2000).- Noninductive Methods of Inference: Direct Inference Instead of Generalization (2000-...).- The Big Picture.