Structural Risk Minimization Structural Risk Minimization (.pdf) Structural risk minimization (SRM) (Vapnik and Chervonekis, 1974) is an inductive principle for model selection used for learning from finite training data sets. It describes a general model of capacity control and provides a trade-off between hypothesis space complexity (the VC dimension of approximating functions) and the …
Sellaholics.com – Find Top 10 Products in Every Category VC Dimension (.pdf) VC Dimension Wikipedia: VC dimension Shatter – Wikipedia VC dimension (for Vapnik Chervonenkis dimension) (Vapnik and Chervonenkis (1968, 1971), Vapnik (1979)) measures the capacity of a hypothesis space. Capacity is a measure of complexity and measures the expressive power, richness or flexibility of a set of …
SVM Tutorials Best Tutorials Introductory: HEARST, Marti A., et al., 1998. Support vector machines. IEEE Intelligent Systems, 13(4), 18–28. Intermediate: BURGES, Christopher J. C., 1998. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 121–167. Advanced: CRISTIANINI, Nello, and John SHAWE-TAYLOR, 2000. An Introduction to Support Vector Machines and Other Kernel-Based Learning Methods. Cambridge, UK: Cambridge …
Training Support Vector Machines BOSER, Bernhard E., Isabelle M. GUYON and Vladimir N. VAPNIK, A Training Algorithm for Optimal Margin Classifiers Colin CAMPBELL and Nello CRISTIANINI, Simple Learning Algorithms for Training Support Vector Machines CAMPBELL, Colin, Algorithmic Approaches to Training Support Vector Machines: A Survey CHANG, Chih-Chung and Chih-Jen LIN, Training ?-Support Vector Classifiers: Theory and Algorithms OSUNA, …
Surveys Algorithmic Approaches to Training Support Vector Machines: A Survey Kernel Methods: A Survey of Current Techniques A Framework for Structural Risk Minimisation
Statistical Learning Theory Wikipedia: Statistical learning theory Wikipedia: Vapnik Chervonenkis theory Most important references VAPNIK, 1995. The nature of statistical learning theory. VAPNIK, V., 1998. Statistical Learning Theory. Wiley, New York. Bibliography , ?.?.?. and ?.?.?. , 2001. ?????????: ??????. ????????. [Cited by 6] (1.18/year) , ?.?.?., 2000. ??????????????. ?????. [Cited by 256] (41.99/year) , ?.?.?., 2000. Introduction to Statistical …
Regularization Regularization (mathematics) – Wikipedia regularization – Google Scholar regularization – Google Search Plan of Class 4 EVGENIOU, Theodoros, et al., Regularization and statistical learning theory for data analysis EVGENIOU, Theodoros, Massimiliano PONTIL and Tomaso POGGIO, A unified framework for Regularization Networks and Support Vector Machines Evgeniou, Pontil and Poggio GIROSI, Federico, Michael JONES and Tomaso …
Support Vector Machines for Regression “The Support Vector method can also be applied to the case of regression, maintaining all the main features that characterise the maximal margin algorithm: a non-linear function is learned by a linear learning machine in a kernel-induced feature space while the capacity of the system is controlled by a …
Quadratic Programming Quadratic programming – Wikipedia “quadratic programming” – Google Scholar “quadratic programming” – Google Search
Performance “In most of these cases, SVM generalization performance (i.e. error rates on test sets) either matches or is significantly better than that of competing methods.” Burgess (1998) “The time complexity of training SVMs scales approximately between quadratic and cubic in the number of training data points [22].” Cao (2003) “Practical experience with such …
