Add to ORKGPackage bmrm implements the ”Bundle Methods for Regularized Risk Mini-mization ” proposed by Teo et al. (2010). This framework efficiently solves a minimization problem encountred in many recent machine learning algorithm where the goal is to minimze a loss function l(w, xi, yi) on the training instance
In this paper, we design a novel regularized empirical risk minimization technique for classificatio...
Supervised classification techniques use training samples to learn a classification rule with small...
We formulate the regression problem as one of maximizing the minimum probability, symbolized by &...
Abstract. Many machine learning algorithms lead to solving a convex regularized risk minimization pr...
Statistical modeling with regularized risk minimization Given some data points xi, i = 1,..., n, lea...
Regularization techniques have become a principled tool for model-based statistics and artificial in...
International audienceWe establish risk bounds for Regularized Empirical Risk Minimizers (RERM) when...
Supervised classification techniques use training samples to learn a classification rule with small ...
We present a globally convergent method for regularized risk minimization prob-lems. Our method appl...
This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied...
A wide variety of machine learning problems can be de-scribed as minimizing a regularized risk funct...
We present an extension of Vapnik's classical empirical risk minimizer (ERM) where the empirical ris...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
spired by work of Teo et al., JMLR 2010. This universal data mining framework is particu-larly usefu...
Abstract. The k-support norm has been recently introduced to perform correlated sparsity regularizat...
In this paper, we design a novel regularized empirical risk minimization technique for classificatio...
Supervised classification techniques use training samples to learn a classification rule with small...
We formulate the regression problem as one of maximizing the minimum probability, symbolized by &...
Abstract. Many machine learning algorithms lead to solving a convex regularized risk minimization pr...
Statistical modeling with regularized risk minimization Given some data points xi, i = 1,..., n, lea...
Regularization techniques have become a principled tool for model-based statistics and artificial in...
International audienceWe establish risk bounds for Regularized Empirical Risk Minimizers (RERM) when...
Supervised classification techniques use training samples to learn a classification rule with small ...
We present a globally convergent method for regularized risk minimization prob-lems. Our method appl...
This paper presents a novel approach for dealing with the structural risk minimization (SRM) applied...
A wide variety of machine learning problems can be de-scribed as minimizing a regularized risk funct...
We present an extension of Vapnik's classical empirical risk minimizer (ERM) where the empirical ris...
This report is a summary of the paper [BM06] of Peter Bartlett and Shahar Mendelson on Empirical Min...
spired by work of Teo et al., JMLR 2010. This universal data mining framework is particu-larly usefu...
Abstract. The k-support norm has been recently introduced to perform correlated sparsity regularizat...
In this paper, we design a novel regularized empirical risk minimization technique for classificatio...
Supervised classification techniques use training samples to learn a classification rule with small...
We formulate the regression problem as one of maximizing the minimum probability, symbolized by &...