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https://link.springer.com/article/10.1023%2FA%3A1018946025316
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines.Cited by: 1431
http://faculty.insead.edu/theodoros-evgeniou/documents/regularization_networks_and_support_vector_machines.pdf
2 T. Evgeniou et al / Regularization Networks and Support Vector Machines lpairs (xi;yi)) and is the regularization parameter (see the seminal work of [102]). Under …
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.411.5681
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Communicated by M. Buhmann Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.95.3582
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.42.1351
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): this paper is to present a theoretical framework for the problem of learning from examples. Learning from examples can be regarded as the regression problem of approximating a multivariate function from sparse data -- and we will take this point of view here
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.377.4123
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular the regression problem of approximating a multivariate function from sparse data. Radial Basis Functions, for example, are a special case of both regularization and Support Vector Machines.
https://direct.mit.edu/books/book/1821/Learning-with-KernelsSupport-Vector-Machines
Bernhard Schölkopf is Director at the Max Planck Institute for Intelligent Systems in Tübingen, Germany. He is coauthor of Learning with Kernels (2002) and is a coeditor of Advances in Kernel Methods: Support Vector Learning (1998), Advances in Large-Margin Classifiers (2000), and Kernel Methods in Computational Biology (2004), all published by the MIT Press.
http://citeseer.ist.psu.edu/viewdoc/summary?doi=10.1.1.3.5956
As a consequence, we are able to theoretically explain the effect of the choice of kernel function on the generalization performance of support vector machines. Documents Authors
https://en.wikipedia.org/wiki/Regularization_perspectives_on_support_vector_machines
Regularization perspectives on support-vector machines provide a way of interpreting support-vector machines (SVMs) in the context of other machine-learning algorithms. SVM algorithms categorize multidimensional data, with the goal of fitting the training set data well, but also avoiding overfitting, so that the solution generalizes to new data points. ...
https://www.researchgate.net/publication/220391260_Regularization_Networks_and_Support_Vector_Machines
Regularization Networks and Support Vector Machines are techniques for solving certain problems of learning from examples – in particular, the regression problem of approximating a multivariate ...
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