
Maximal Margin Distribution Support Vector Regression with coupled Constraintsbased Convex Optimization
Support vector regression (SVR) is one of the most popular machine learn...
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An Epsilon Hierarchical Fuzzy Twin Support Vector Regression
The research presents epsilon hierarchical fuzzy twin support vector reg...
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A working likelihood approach to support vector regression with a datadriven insensitivity parameter
The insensitive parameter in support vector regression determines the se...
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Scenario optimization with relaxation: a new tool for design and application to machine learning problems
Scenario optimization is by now a well established technique to perform ...
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Sufficient Conditions for a Linear Estimator to be a Local Polynomial Regression
It is shown that any linear estimator that satisfies the moment conditio...
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Linear Matrix Inequality Approaches to Koopman Operator Approximation
The regression problem associated with finding a matrix approximation of...
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Predicting the Critical Number of Layers for Hierarchical Support Vector Regression
Hierarchical support vector regression (HSVR) models a function from dat...
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Linear Support Vector Regression with Linear Constraints
This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when the kernel is linear. Adding those constraints into the problem allows to add prior knowledge on the estimator obtained, such as finding probability vector or monotone data. We propose a generalization of the Sequential Minimal Optimization (SMO) algorithm for solving the optimization problem with linear constraints and prove its convergence. Then, practical performances of this estimator are shown on simulated and real datasets with different settings: non negative regression, regression onto the simplex for biomedical data and isotonic regression for weather forecast.
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