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Small-sample Machine Learning Theory: Statistical Learning Theory

《南京理工大学学报》(自然科学版)[ISSN:1005-9830/CN:32-1397/N]

Issue:
2001年01期
Page:
108-112
Research Field:
Publishing date:

Info

Title:
Small-sample Machine Learning Theory: Statistical Learning Theory
Author(s):
TanDongnin TanDonghan①
Electronic 55 th Institude,Ministry of Information Industry,Nanjing 210016)
Keywords:
sample trees stat ist ical est imat ion pattern recognit ion statist ical learning theory machine learning
PACS:
TP18
DOI:
-
Abstract:
Stat ist ical Learning T heory, a recent ly developed new theory for pattern recogn-i t ion, is a smal-l sample stat ist ics proposed by Vapnik et al, w hich deals mainly w ith the statist ic principles w hen samples are limited, especially to describe the propert ies of learning procedure in such cases. It provides us a new framework for the smal-l sample learning problem, and also a novel powerful learning method called Support Vector Machine, w hich can solve smal-l sample learning problem better. T his paper w ill introduce the basic ideas of the theory, its major characterist ics, some current research t rends of it and some thinking from us about it .

References:

1 Vapnik V N. Estimation of dependencies based o n empir ical data. Berlin: Spr ing er-Verlag , 1982
2 Vapnik V N. The nature of statistical learning theory. New York: Spr inger-Verlag, 1995
3 Cherkassky V, Mulier F. Learning from data: concepts, theory and met hods. New York: Jo hn Viley& Sons, 1997
4 加肇祺1 模式识别1 北京: 清华大学出版社, 1988
5 Vapnik V N, Lev in E, Le Cun Y. Measuring the VC-dimensio n of a learning machine. Neural Computation, M IT Press, 1994( 6) : 851~ 876

Memo

Memo:
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Last Update: 2013-03-25