Add to ORKGSequential Minimal Optimization (SMO) is one of the most popular and fast algorithm that solves the Support Vector Machine (SVM) training problem. However, SMO requires a large amount of computation time for solving large size problems, thus the interest in developing an efficient parallel implementation. In this paper, we analyzed two different parallel implementations of this algorithm, and also two different development tools: gcc+MPICH and Intel’s icc+MPI. We experimentally evaluated that there is not an optimal solution for all the cases, in particular considering the present heterogeneous computing platform
In the recent years support vector machines (SVMs) have been successfully applied to solve a large n...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
International audienceThis paper proposes a new and efficient parallel implementation of support vec...
This paper proposes a parallel FPGA implementation of the training phase of a Support Vector Machine...
We propose in this work a nested version of the well\u2013known Sequential Minimal Optimization (SMO...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonst...
In this paper, we evaluate the performance of various parallel optimization meth-ods for Kernel Supp...
Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To g...
The sequential minimal optimization (SMO) algorithm is a popular algorithm used to solve the support...
Máquinas de aprendizagem, como Redes Neuronais Artificiais (ANNs), Redes Bayesianas, Máquinas de Vet...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vecto...
This article points out an important source of inefficiency in Platt's sequential minimal optimizati...
In the recent years support vector machines (SVMs) have been successfully applied to solve a large n...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
International audienceThis paper proposes a new and efficient parallel implementation of support vec...
This paper proposes a parallel FPGA implementation of the training phase of a Support Vector Machine...
We propose in this work a nested version of the well\u2013known Sequential Minimal Optimization (SMO...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonst...
In this paper, we evaluate the performance of various parallel optimization meth-ods for Kernel Supp...
Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To g...
The sequential minimal optimization (SMO) algorithm is a popular algorithm used to solve the support...
Máquinas de aprendizagem, como Redes Neuronais Artificiais (ANNs), Redes Bayesianas, Máquinas de Vet...
Training a Support Vector Machine (SVM) requires the solution of a very large quadratic programming...
The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and re...
Sequential minimal optimization (SMO) is quite an efficient algorithm for training the support vecto...
This article points out an important source of inefficiency in Platt's sequential minimal optimizati...
In the recent years support vector machines (SVMs) have been successfully applied to solve a large n...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
International audienceThis paper proposes a new and efficient parallel implementation of support vec...