Add to ORKGA sparse representation of Support Vector Machines (SVMs) with respect to input features is desirable for many applications. In this paper, by introducing a 0-1 control variable to each input feature, Zo-norm Sparse SVM (SSVM) is converted to a mixed integer programming (MIP) problem. Rather than directly solving this MIP, we propose an efficient cutting plane algorithm combining with multiple kernel learning to solve its convex relaxation. A global convergence proof for our method is also presented. Comprehensive experimental results on one synthetic and l0 real world datasets show that our proposed method can obtain better or competitive performance compared with existing SVM-based feature selection methods in term of sparsity and general...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
This work deals with the Support Vector Machine (SVM) learning process which, as it is well-known, c...
Feature selection is an effective way to reduce computational cost and improve feature quality for t...
Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in...
This paper introduces 1 a new support vector machine (SVM) formulation to obtain sparse solutions in...
Many Kernel Learning Algorithms(KLA), including Support Vector Machine (SVM), result in a Kernel Mac...
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for M...
International audienceFeature selection in learning to rank has recently emerged as a crucial issue....
The support vector machines (SVMs) have been very successful in many machine learning problems. Howe...
The performance of classification methods, such as Support Vector Machines, depends heavily on the p...
In this work we consider feature selection for two-class linear models, a challenging task arising i...
This work focuses on support vector machine (SVM)with feature selection. A MILP formulation is propo...
Embedding feature selection in nonlinear SVMs leads to a challenging non-convex minimization problem...
<p>We propose a new binary classification and variable selection technique especially designed for h...
© Springer International Publishing AG 2017. Performing predictions using a non-linear support vecto...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
This work deals with the Support Vector Machine (SVM) learning process which, as it is well-known, c...
Feature selection is an effective way to reduce computational cost and improve feature quality for t...
Kernel-based methods for support vector machines (SVM) have shown highly advantageous performance in...
This paper introduces 1 a new support vector machine (SVM) formulation to obtain sparse solutions in...
Many Kernel Learning Algorithms(KLA), including Support Vector Machine (SVM), result in a Kernel Mac...
We propose a direct approach to learning sparse Support Vector Machine (SVM) prediction models for M...
International audienceFeature selection in learning to rank has recently emerged as a crucial issue....
The support vector machines (SVMs) have been very successful in many machine learning problems. Howe...
The performance of classification methods, such as Support Vector Machines, depends heavily on the p...
In this work we consider feature selection for two-class linear models, a challenging task arising i...
This work focuses on support vector machine (SVM)with feature selection. A MILP formulation is propo...
Embedding feature selection in nonlinear SVMs leads to a challenging non-convex minimization problem...
<p>We propose a new binary classification and variable selection technique especially designed for h...
© Springer International Publishing AG 2017. Performing predictions using a non-linear support vecto...
The file attached to this record is the author's final peer reviewed version. The Publisher's final ...
This work deals with the Support Vector Machine (SVM) learning process which, as it is well-known, c...
Feature selection is an effective way to reduce computational cost and improve feature quality for t...