Nov 30, 2017 · In this paper, we present a new approach to obtain sparse support vector machines (SVM) based on simulated annealing (SA), named SATE.
It was proposed a method to solve the dual quadratic optimization problem of SVMs.The proposal named SATE is based on simulated annealing.
In this paper, a number of problems and shortcomings that may be encountered when implementing the converging linear particle swarm optimisation algorithm ...
In this paper, we present a new approach to obtain sparse support vector machines (SVM) based on simulated annealing (SA), named SATE. In our proposal, SA was ...
In this paper, we present a new approach to obtain sparse support vector machines (SVM) based on simulated annealing (SA), named SATE. In our proposal, SA was ...
It was proposed a method to solve the dual quadratic optimization problem of SVMs. • The proposal named SATE is based on simulated annealing. • The objective ...
Training soft margin support vector machines by simulated annealing: A dual approach. June 2017 · Expert Systems with Applications.
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"Training soft margin support vector machines by simulated annealing: A dual approach." Expert Systems with Applications 87 (November 2017): 157–69. http ...
up with soft-margin support vector machines through simulated annealing. Our proposal called SATE is able to handle the dual optimization problem and then.
In this work we propose to use simulated annealing as a method ... A Novel Simulated Annealing-Based Learning Algorithm for Training Support Vector Machines.