This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal optimization (SMO) algorithm for support vector machine (SVM)regression that is caused by the use of a single threshold value. Using clues from the KKT conditions for the dual problem, two threshold parameters are employed to derive modifications of SMO for regression, These modified algorithms perform significantly faster than the original SMO on the datasets tried
Proceedings of the International Joint Conference on Neural Networks32088-209385OF
Three novel algorithms are presented; the linear programming (LP) machine for pattern classification...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal...
This article points out an important source of inefficiency in Platt's sequential minimal optimizati...
The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonst...
The sequential minimal optimization (SMO) algorithm is a popular algorithm used to solve the support...
In the recent years support vector machines (SVMs) have been successfully applied to solve a large n...
This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when th...
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...
Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To g...
Seven classifiers are compared on sixteen quite different, standard and extensively used datasets in...
The standard Support Vector Machine formulation does not provide its user with the ability to explic...
S V N Vishwanathan of Purdue University presented a lecture on April 15, 2011 from 2:00 pm - 3:00 p...
Proceedings of the International Joint Conference on Neural Networks32088-209385OF
Three novel algorithms are presented; the linear programming (LP) machine for pattern classification...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
This paper points out an important source of inefficiency in Smola and Scholkopfs sequential minimal...
This article points out an important source of inefficiency in Platt's sequential minimal optimizati...
The Support Vector Machine is a widely employed machine learning model due to its repeatedly demonst...
The sequential minimal optimization (SMO) algorithm is a popular algorithm used to solve the support...
In the recent years support vector machines (SVMs) have been successfully applied to solve a large n...
This paper studies the addition of linear constraints to the Support Vector Regression (SVR) when th...
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...
Support Vector Machines (SVM) is a practical algorithm that has been widely used in many areas. To g...
Seven classifiers are compared on sixteen quite different, standard and extensively used datasets in...
The standard Support Vector Machine formulation does not provide its user with the ability to explic...
S V N Vishwanathan of Purdue University presented a lecture on April 15, 2011 from 2:00 pm - 3:00 p...
Proceedings of the International Joint Conference on Neural Networks32088-209385OF
Three novel algorithms are presented; the linear programming (LP) machine for pattern classification...
Starting from a reformulation of Cramer & Singer Mul- ticlass Kernel Machine, we propose a Sequentia...
Add to ORKG