Add to ORKGInternational audienceIn the field of machine learning, multi-class support vector machines (M-SVMs) are state-of-the-art classifiers with training algorithms that amount to convex quadratic programs. However, solving these quadratic programs in practice is a complex task that typically cannot be assigned to a general purpose solver. The paper describes the main features of an efficient solver for M-SVMs, as implemented in the MSVMpack software. The latest additions to this software are also highlighted and a few numerical experiments are presented to assess its efficiency
Winner-take-all multiclass classifiers are built on the top of a set of prototypes each representing...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either ...
International audienceIn the field of machine learning, multi-class support vector machines (M-SVMs)...
Abstract We present new decomposition algorithms for training multi-class support vector machines (S...
Lately, Support Vector Machine (SVM) methods have become a very popular technique in the machine le...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
We re-visit Support Vector Machines (SVMs) and provide a novel interpretation thereof in terms of we...
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP...
International audienceThis paper describes MSVMpack, an open source software package dedicated to ou...
We present new decomposition algorithms for training multi-class support vector machines (SVMs), in ...
Training a support vector machine (SVM) requires the solution of a quadratic programming problem (QP...
Support Vectors (SV) are a machine learning procedure based on Vapnik’s Statistical Learning Theory,...
Model selection is of major interest in statistical learning. In this document, we introduce model s...
Winner-take-all multiclass classifiers are built on the top of a set of prototypes each representing...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either ...
International audienceIn the field of machine learning, multi-class support vector machines (M-SVMs)...
Abstract We present new decomposition algorithms for training multi-class support vector machines (S...
Lately, Support Vector Machine (SVM) methods have become a very popular technique in the machine le...
Support Vector Machines (SVMs) are state-of-the-art learning algorithms forclassification problems d...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
We re-visit Support Vector Machines (SVMs) and provide a novel interpretation thereof in terms of we...
Training a Support Vector Machine (SVM) requires the solution of a quadratic programming problem (QP...
International audienceThis paper describes MSVMpack, an open source software package dedicated to ou...
We present new decomposition algorithms for training multi-class support vector machines (SVMs), in ...
Training a support vector machine (SVM) requires the solution of a quadratic programming problem (QP...
Support Vectors (SV) are a machine learning procedure based on Vapnik’s Statistical Learning Theory,...
Model selection is of major interest in statistical learning. In this document, we introduce model s...
Winner-take-all multiclass classifiers are built on the top of a set of prototypes each representing...
Support Vector Machine is a powerful classification technique based on the idea of Structural risk m...
Traditional extensions of the binary support vector machine (SVM) to multiclass problems are either ...