Li W., Dai D., Tan M., Xu D., Van Gool L., ''Fast algorithms for linear and kernel SVM+'', 29th IEEE conference on computer vision and pattern recognition - CVPR 2016, 9 pp., June 26 - July 1, 2016, Las Vegas, Nevada, USA.status: publishe
Training a support vector machine on a data set of huge size with thousands of classes is a challeng...
This paper presents a novel application of automata algorithms to machine learning. It introduces th...
International audienceWe propose a new algorithm for training a linear Support Vector Machine in the...
For classification problems with millions of training examples or dimensions, accuracy, training and...
For classification problems with millions of training examples or dimensions, accuracy, training and...
AbstractThis paper is contributed to object recognition with linear Support Vector Machine (SVM), fr...
Support vector machines (SVMs) are very popular methods for solving classification problems that req...
In this paper, we improve the efficiency of kernelized support vector machine (SVM) for image classi...
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In t...
Training machine learning models sometimes needs to be done on large amounts of data that exceed the...
* Both first authors contributed equally. Abstract. We propose to learn the kernel of an SVM as the ...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Recently two kinds of reduction techniques which aimed at saving training time for SVM problems with...
Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Ma...
Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Ma...
Training a support vector machine on a data set of huge size with thousands of classes is a challeng...
This paper presents a novel application of automata algorithms to machine learning. It introduces th...
International audienceWe propose a new algorithm for training a linear Support Vector Machine in the...
For classification problems with millions of training examples or dimensions, accuracy, training and...
For classification problems with millions of training examples or dimensions, accuracy, training and...
AbstractThis paper is contributed to object recognition with linear Support Vector Machine (SVM), fr...
Support vector machines (SVMs) are very popular methods for solving classification problems that req...
In this paper, we improve the efficiency of kernelized support vector machine (SVM) for image classi...
Most literature on Support Vector Machines (SVMs) concentrate on the dual optimization problem. In t...
Training machine learning models sometimes needs to be done on large amounts of data that exceed the...
* Both first authors contributed equally. Abstract. We propose to learn the kernel of an SVM as the ...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Recently two kinds of reduction techniques which aimed at saving training time for SVM problems with...
Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Ma...
Image classification is intrinsically a multiclass, nonlinear classification task. Support Vector Ma...
Training a support vector machine on a data set of huge size with thousands of classes is a challeng...
This paper presents a novel application of automata algorithms to machine learning. It introduces th...
International audienceWe propose a new algorithm for training a linear Support Vector Machine in the...
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