From Regularization Operators to Support Vector Kernels
نویسندگان
چکیده
We derive the correspondence between regularization operators used in Regularization Networks and Hilbert Schmidt Kernels appearing in Support VectorMachines. More specifically, we prove that the Green’s Functions associated with regularization operators are suitable Support Vector Kernels with equivalent regularization properties. As a by–product we show that a large number of Radial Basis Functions namely conditionally positive definite functions may be used as Support Vector kernels.
منابع مشابه
The connection between regularization operators and support vector kernels
In this paper a correspondence is derived between regularization operators used in regularization networks and support vector kernels. We prove that the Green's Functions associated with regularization operators are suitable support vector kernels with equivalent regularization properties. Moreover, the paper provides an analysis of currently used support vector kernels in the view of regulariz...
متن کاملKnowledge-Based Green’s Kernel for Support Vector Regression
This paper presents a novel prior knowledge-based Green’s kernel for support vector regression SVR . After reviewing the correspondence between support vector kernels used in support vector machines SVMs and regularization operators used in regularization networks and the use of Green’s function of their corresponding regularization operators to construct support vector kernels, a mathematical ...
متن کاملEnsemble Kernel Learning Model for Prediction of Time Series Based on the Support Vector Regression and Meta Heuristic Search
In this paper, a method for predicting time series is presented. Time series prediction is a process which predicted future system values based on information obtained from past and present data points. Time series prediction models are widely used in various fields of engineering, economics, etc. The main purpose of using different models for time series prediction is to make the forecast with...
متن کاملRegularizing multiple kernel learning using response surface methodology
In recent years, several methods have been proposed to combine multiple kernels using a weighted linear sum of kernels. These different kernels may be using information coming from multiple sources or may correspond to using different notions of similarity on the same source. We note that such methods, in addition to the usual ones of the canonical support vector machine formulation, introduce ...
متن کاملEntropy Numbers, Operators and Support Vector Kernels
We derive new bounds for the generalization error of feature space machines, such as support vector machines and related regularization networks by obtaining new bounds on their covering numbers. The proofs are based on a viewpoint that is apparently novel in the field of statistical learning theory. The hypothesis class is described in terms of a linear operator mapping from a possibly infinit...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 1997