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Support Vector Machines,Kernel Logistic Regression,and Boosting
Support Vector Machines Kernel Logistic Regression Boosting
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2015/8/21
Support Vector Machines,Kernel Logistic Regression,and Boosting.
Complex Support Vector Machines for Regression and Quaternary Classification
Support Vector Machines Kernel methods Widely linear estimation com-plex data
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2013/4/28
We present a support vector machines (SVM) rationale suitable for regression and quaternary classification problems that use complex data, exploiting the notions of widely linear estimation and pure c...
An Equivalence between the Lasso and Support Vector Machines
Equivalence the Lasso Support Vector Machines
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2013/4/28
We investigate the relation of two fundamental tools in machine learning, that is the support vector machine (SVM) for classification, and the Lasso technique used in regression. We show that the resu...
Asymptotic Normality of Support Vector Machines for Classification and Regression
Nonparametric regression support vector machines asymptotic normality
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2010/10/14
In nonparametric classification and regression problems, support vector machines (SVMs) attract much attention in theoretical and in applied statistics. In an abstract sense, SVMs can be seen as regu...
Structured variable selection in support vector machines
Classification Heredity Nonparametric estimation Support vector machine Variable selection
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2009/9/16
When applying the support vector machine (SVM) to high-dimensional classification problems, we often impose a sparse structure in the SVM to eliminate the influences of the irrelevant predictors. The ...
Consistency of support vector machines for forecasting the evolution of an unknown ergodic dynamical system from observations with unknown noise
Observational noise model forecasting dynamical systems support vector machines consistency
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2010/4/30
We consider the problem of forecasting the next (observable)
state of an unknown ergodic dynamical system from a noisy observation
of the present state. Our main result shows, for example, that
sup...