Structural risk minimization (SRM) is an inductive principle of use in machine learning

Machine learning

Machine learning, a branch of artificial intelligence, is a scientific discipline concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases...

. Commonly in machine learning, a generalized model must be selected from a finite data set, with the consequent problem of overfitting

Overfitting

In statistics, overfitting occurs when a statistical model describes random error or noise instead of the underlying relationship. Overfitting generally occurs when a model is excessively complex, such as having too many parameters relative to the number of observations...

 – the model becoming too strongly tailored to the particularities of the training set and generalizing poorly to new data. The SRM principle addresses this problem by balancing the model's complexity against its success at fitting the training data.

The SRM principle was first set out in a 1974 paper by Vladimir Vapnik

Vladimir Vapnik

Vladimir Naumovich Vapnik is one of the main developers of Vapnik–Chervonenkis theory. He was born in the Soviet Union. He received his master's degree in mathematics at the Uzbek State University, Samarkand, Uzbek SSR in 1958 and Ph.D in statistics at the Institute of Control Sciences, Moscow in...

 and Alexey Chervonenkis

Alexey Chervonenkis

Alexey Jakovlevich Chervonenkis is a Soviet and Russian mathematician, and, with Vladimir Vapnik, was one of the main developers of the Vapnik–Chervonenkis theory, also known as the "fundamental theory of learning" an important part of computational learning theory. As of September 2007, Dr...

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External links

• Structural risk minimization at the support vector machines website.