Adjustable entropy function method for support vector machine

2008 ◽  
Vol 19 (5) ◽  
pp. 1029-1034 ◽  
Author(s):  
Wu Qing ◽  
Liu Sanyang ◽  
Zhang Leyou
2021 ◽  
Vol 2021 ◽  
pp. 1-12
Author(s):  
Yaming Ren

With the continuous development of the world economy, the development and utilization of environmentally friendly and renewable energy have become the trend in many countries. In this paper, we study the dynamic economic dispatch with wind integrated. Firstly, we take advantage of the positive and negative spinning reserve to deal with wind power output prediction errors in order to establish a dynamic economic dispatch model of wind integrated. The existence of a min function makes the dynamic economic dispatch model nondifferentiable, which results in the inability to directly use the traditional mathematical methods based on gradient information to solve the model. Inspired by the aggregate function, we can easily transform the nondifferentiable model into a smooth model when parameter p tends to infinity. However, the aggregate function will cause data overflow when p tends to infinity. Then, for solving this problem, we take advantage of the adjustable entropy function method to replace of aggregate function method. In addition, we further discuss the adjustable entropy function method and point out that the solution generated by the adjustable entropy function method can effectively approximate the solution of the original problem without parameter p tending to infinity. Finally, simulation experiments are given, and the simulation results prove the effectiveness and correctness of the adjustable entropy function method.


2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Jinjin Liang ◽  
De Wu

Based on diagonal weighted support vector machine, a smooth model with Newton algorithm is proposed and is called SDWNSVM for short. SDWNSVM introduces the entropy function to approximate the plus function of the slack in the diagonal weighted SVM and is thus different from traditional SSVM that treats a reformulation problem. SDWNSVM utilizes the dual technique to rewrite the objection function by the connotative relation between the primal and dual program, which induces an exact smooth program and differs from traditional SSVM that uses Lagrangian multipliers to roughly substitute for the hyperplane weight. SDWNSVM proves the equivalence between the obtained model and the original one and proposes Newton algorithm to figure out the optimal solution. Numerical experiments on UCI data demonstrate that SDWNSVM has higher accuracies and less iteration than existing methods.


2020 ◽  
Author(s):  
V Vasilevska ◽  
K Schlaaf ◽  
H Dobrowolny ◽  
G Meyer-Lotz ◽  
HG Bernstein ◽  
...  

2019 ◽  
Vol 15 (2) ◽  
pp. 275-280
Author(s):  
Agus Setiyono ◽  
Hilman F Pardede

It is now common for a cellphone to receive spam messages. Great number of received messages making it difficult for human to classify those messages to Spam or no Spam.  One way to overcome this problem is to use Data Mining for automatic classifications. In this paper, we investigate various data mining techniques, named Support Vector Machine, Multinomial Naïve Bayes and Decision Tree for automatic spam detection. Our experimental results show that Support Vector Machine algorithm is the best algorithm over three evaluated algorithms. Support Vector Machine achieves 98.33%, while Multinomial Naïve Bayes achieves 98.13% and Decision Tree is at 97.10 % accuracy.


2011 ◽  
Vol 131 (8) ◽  
pp. 1495-1501
Author(s):  
Dongshik Kang ◽  
Masaki Higa ◽  
Hayao Miyagi ◽  
Ikugo Mitsui ◽  
Masanobu Fujita ◽  
...  

Author(s):  
Ryoichi ISAWA ◽  
Tao BAN ◽  
Shanqing GUO ◽  
Daisuke INOUE ◽  
Koji NAKAO

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