Computational performance optimization of support vector machine based on support vectors

2016 ◽  
Vol 211 ◽  
pp. 66-71 ◽  
Author(s):  
Xuesong Wang ◽  
Fei Huang ◽  
Yuhu Cheng
Keyword(s):  
Support Vector Machine ◽  
Performance Optimization ◽  
Support Vector ◽  
Computational Performance ◽  
Support Vectors

Financial Distress Prediction Based on Support Vector Machine with a Modified Kernel Function

2016 ◽  
Vol 25 (3) ◽  
pp. 417-429
Author(s):  
Chong Wu ◽  
Lu Wang ◽  
Zhe Shi
Keyword(s):  
Support Vector Machine ◽  
Kernel Function ◽  
Financial Distress ◽  
Classification Accuracy ◽  
Feature Space ◽  
Support Vector ◽  
Input Space ◽  
Financial Distress Prediction ◽  
Support Vectors ◽  
Distress Prediction

AbstractFor the financial distress prediction model based on support vector machine, there are no theories concerning how to choose a proper kernel function in a data-dependent way. This paper proposes a method of modified kernel function that can availably enhance classification accuracy. We apply an information-geometric method to modifying a kernel that is based on the structure of the Riemannian geometry induced in the input space by the kernel. A conformal transformation of a kernel from input space to higher-dimensional feature space enlarges volume elements locally near support vectors that are situated around the classification boundary and reduce the number of support vectors. This paper takes the Gaussian radial basis function as the internal kernel. Additionally, this paper combines the above method with the theories of standard regularization and non-dimensionalization to construct the new model. In the empirical analysis section, the paper adopts the financial data of Chinese listed companies. It uses five groups of experiments with different parameters to compare the classification accuracy. We can make the conclusion that the model of modified kernel function can effectively reduce the number of support vectors, and improve the classification accuracy.

scholarly journals Improved Instance Selection Methods for Support Vector Machine Speed Optimization

2017 ◽  
Vol 2017 ◽  
pp. 1-11 ◽  
Author(s):  
Andronicus A. Akinyelu ◽  
Aderemi O. Adewumi
Keyword(s):  
Support Vector Machine ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Instance Selection ◽  
Training Time ◽  
Svm Classification ◽  
Support Vectors ◽  
Increase With Increase ◽  
Dataset Size ◽  
Machine Speed

Support vector machine (SVM) is one of the top picks in pattern recognition and classification related tasks. It has been used successfully to classify linearly separable and nonlinearly separable data with high accuracy. However, in terms of classification speed, SVMs are outperformed by many machine learning algorithms, especially, when massive datasets are involved. SVM classification speed scales linearly with number of support vectors, and support vectors increase with increase in dataset size. Hence, SVM classification speed can be enormously reduced if it is trained on a reduced dataset. Instance selection techniques are one of the most effective techniques suitable for minimizing SVM training time. In this study, two instance selection techniques suitable for identifying relevant training instances are proposed. The techniques are evaluated on a dataset containing 4000 emails and results obtained compared to other existing techniques. Result reveals excellent improvement in SVM classification speed.

A Multi-Classified Method of Support Vector Machine (SVM) Based on Entropy

2012 ◽  
Vol 241-244 ◽  
pp. 1629-1632 ◽  
Author(s):  
Yan Yue
Keyword(s):  
Support Vector Machine ◽  
Information Entropy ◽  
Recognition Rate ◽  
Support Vector ◽  
Entropy Theory ◽  
Classification Rate ◽  
Svm Classification ◽  
Computational Load ◽  
Support Vectors ◽  
Reduction Coefficient

Studies propose to combine standard SVM classification with the information entropy to increase SVM classification rate as well as reduce computational load of SVM testing. The algorithm uses the information entropy theory to per-treat samples’ attributes, and can eliminate some attributes which put small impacts on the date classification by introducing the reduction coefficient, and then reduce the amount of support vectors. The results show that this algorithm can reduce the amount of support vectors in the process of the classification with support vector machine, and heighten the recognition rate when the amount of the samples is larger compared to standard SVM and DAGSVM.

Recognize Fabric Detects Based on Support Vector Machine

2011 ◽  
Vol 332-334 ◽  
pp. 1193-1197
Author(s):  
Xiao Ding Gao ◽  
Gang Wang ◽  
Zhao Ming Nie
Keyword(s):  
Support Vector Machine ◽  
Support Vector ◽  
Statistic Method ◽  
Support Vectors

Recognize Fabric Detects based on support vector machine is established for examining and classifying a variety of fabric Detects by machine. By dimensional histogram statistic method we get parameters and then put the parameters in training example set to get support vectors. The results show this method is feasible and effective.

scholarly journals Optimal Boundary SVM Incremental Learning Algorithm

2013 ◽  
Vol 347-350 ◽  
pp. 2957-2962 ◽  
Author(s):  
Jian Cao ◽  
Shi Yu Sun ◽  
Xiu Sheng Duan
Keyword(s):  
Support Vector Machine ◽  
Learning Process ◽  
Incremental Learning ◽  
Learning Algorithm ◽  
Identical Result ◽  
Support Vector ◽  
Optimal Boundary ◽  
Support Vectors ◽  
Sample Classification ◽  
Depth Analysis

Support vectors (SVs) cant be selected completely in support vector machine (SVM) incremental, resulting incremental learning process cant be sustained. In order to solve this problem, the article proposes optimal boundary SVM incremental learning algorithm. Based on in-depth analysis of the trend of the classification surface and make use of the KKT conditions, selecting the border of the vectors include the support vectors to participate SVM incremental learning. The experiment shows that the algorithm can be completely covered the support vectors and have the identical result with the classic support vector machine, it also saves lots of time. Therefore it can provide the conditions for future large sample classification and incremental learning sustainability.

scholarly journals Training v-Support Vector Classifiers: Theory and Algorithms

2001 ◽  
Vol 13 (9) ◽  
pp. 2119-2147 ◽  
Author(s):  
Chih-Chung Chang ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machine ◽  
Numerical Experiments ◽  
Decomposition Method ◽  
Large Scale ◽  
Optimal Solution ◽  
Solution Set ◽  
Support Vector ◽  
Support Vectors ◽  
Optimal Solution Set

The ν-support vector machine (ν-SVM) for classification proposed by Schölkopf, Smola, Williamson, and Bartlett (2000) has the advantage of using a parameter ν on controlling the number of support vectors. In this article, we investigate the relation between ν-SVM and C-SVM in detail. We show that in general they are two different problems with the same optimal solution set. Hence, we may expect that many numerical aspects of solving them are similar. However, compared to regular C-SVM, the formulation of ν-SVM is more complicated, so up to now there have been no effective methods for solving large-scale ν-SVM. We propose a decomposition method for ν-SVM that is competitive with existing methods for C-SVM. We also discuss the behavior of ν-SVM by some numerical experiments.

scholarly journals Komparasi Algoritma Naïve Bayes dan Support Vectors Machine pada Analisis Sentimen SMS HAM dan SPAM

2021 ◽  
Vol 4 (2) ◽  
pp. 249-258
Author(s):  
Lila Dini Utami ◽  
◽  
Lestari Yusuf ◽  
Dini Nurlaela ◽  
◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Mobile Phones ◽  
Naive Bayes ◽  
Naïve Bayes ◽  
Support Vector ◽  
Classification Methods ◽  
Phone Number ◽  
Support Vectors ◽  
Official Information

SMS is a form of communication in the form of messages sent using mobile phones between the designated numbers. SMS is now rarely used because many of the features that have changed are used by chat applications. However, the SMS feature was not removed for one thing, official messages from various applications for leveraging or other official information still use SMS as a sign that the phone number used is there. However, since 2011 there have been so many misuses of this function, so it is suspected that many frauds use SMS as a tool to influence victims. This sms category goes to SMS spam. Therefore, SMS needs to be classified so that users can find out that the SMS is included in the category of Spam or ham (the opposite of spam). Using 400 datasets taken from the UCI repository which is divided into two classes, namely spam and ham, we compare two classification methods, namely Naive Bayes and Support vector Machine in order to get SMS filtering correctly. And after the calculations are done, the accuracy is obtained in Naive Bayes, which is 90.00% Support Vector Machine 81.00%.

scholarly journals Support vector machine and its difficulties from control field of view

2021 ◽  
pp. 014233122097743
Author(s):  
Maryam Yalsavar ◽  
Paknoosh Karimaghaei ◽  
Akbar Sheikh-Akbari ◽  
Pancham Shukla ◽  
Peyman Setoodeh
Keyword(s):  
Support Vector Machine ◽  
Large Scale ◽  
Regularization Parameter ◽  
Experimental Results ◽  
Support Vector ◽  
Support Vectors ◽  
Kernel Parameter ◽  
Wide Range ◽  
Benchmark Datasets ◽  
Training Error

The application of the support vector machine (SVM) classification algorithm to large-scale datasets is limited due to its use of a large number of support vectors and dependency of its performance on its kernel parameter. In this paper, SVM is redefined as a control system and iterative learning control (ILC) method is used to optimize SVM’s kernel parameter. The ILC technique first defines an error equation and then iteratively updates the kernel function and its regularization parameter using the training error and the previous state of the system. The closed loop structure of the proposed algorithm increases the robustness of the technique to uncertainty and improves its convergence speed. Experimental results were generated using nine standard benchmark datasets covering a wide range of applications. Experimental results show that the proposed method generates superior or very competitive results in term of accuracy than those of classical and state-of-the-art SVM based techniques while using a significantly smaller number of support vectors.

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