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Structural risk minimization

Known as: SRM 
Structural risk minimization (SRM) is an inductive principle of use in machine learning. Commonly in machine learning, a generalized model must be… Expand
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Papers overview

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Highly Cited
2011
Highly Cited
2011
Support vector regression has been applied to stock market forecasting problems. However, it is usually needed to tune manually… Expand
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Highly Cited
2010
Highly Cited
2010
Recent advancement in signal processing and information technology has resulted in the use of multiple sensors for the effective… Expand
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2008
2008
Structural risk minimization (SRM) (Vapnik and Chervonenkis 1974) is an inductive principle for model selection used for learning… Expand
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Highly Cited
2005
Highly Cited
2005
Bankruptcy prediction has drawn a lot of research interests in previous literature, and recent studies have shown that machine… Expand
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Highly Cited
2001
Highly Cited
2001
We suggest a penalty function to be used in various problems of structural risk minimization. This penalty is data dependent and… Expand
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Highly Cited
2000
Highly Cited
2000
The Vicinal Risk Minimization principle establishes a bridge between generative models and methods derived from the Structural… Expand
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Highly Cited
1998
Highly Cited
1998
The paper introduces some generalizations of Vapnik's (1982) method of structural risk minimization (SRM). As well as making… Expand
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Highly Cited
1995
Highly Cited
1995
We report a novel possibility for extracting a small subset of a data base which contains all the information necessary to solve… Expand
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Highly Cited
1991
Highly Cited
1991
Learning is posed as a problem of function estimation, for which two principles of solution are considered: empirical risk… Expand
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Highly Cited
1991
Highly Cited
1991
The method of Structural Risk Minimization refers to tuning the capacity of the classifier to the available amount of training… Expand
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