A fast training algorithm for support vector machine via boundary sample selection

2003 ◽  
Author(s):  
Xia Jiantao ◽  
He Mingyi ◽  
Wang Yuying ◽  
Feng Yan
Keyword(s):  
Support Vector Machine ◽  
Sample Selection ◽  
Support Vector ◽  
Training Algorithm ◽  
Fast Training

scholarly journals A Novel Sparse Least Squares Support Vector Machines

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Xiao-Lei Xia ◽  
Weidong Jiao ◽  
Kang Li ◽  
George Irwin
Keyword(s):  
Support Vector Machine ◽  
Least Squares ◽  
Regularization Parameter ◽  
Support Vector ◽  
Training Algorithm ◽  
Least Squares Approximation ◽  
Generalization Ability ◽  
Vector Machines ◽  
Training Cost ◽  
Benchmark Datasets

The solution of a Least Squares Support Vector Machine (LS-SVM) suffers from the problem of nonsparseness. The Forward Least Squares Approximation (FLSA) is a greedy approximation algorithm with a least-squares loss function. This paper proposes a new Support Vector Machine for which the FLSA is the training algorithm—the Forward Least Squares Approximation SVM (FLSA-SVM). A major novelty of this new FLSA-SVM is that the number of support vectors is the regularization parameter for tuning the tradeoff between the generalization ability and the training cost. The FLSA-SVMs can also detect the linear dependencies in vectors of the input Gramian matrix. These attributes together contribute to its extreme sparseness. Experiments on benchmark datasets are presented which show that, compared to various SVM algorithms, the FLSA-SVM is extremely compact, while maintaining a competitive generalization ability.

scholarly journals A New Approach for Remote Sensing Image Sample Selection Based on Convex Theory

2015 ◽  
Vol 11 (4) ◽  
pp. 14 ◽  
Author(s):  
Pan Xin ◽  
Hongbin Sun
Keyword(s):  
Remote Sensing ◽  
Support Vector Machine ◽  
Land Cover ◽  
Sample Selection ◽  
Region Of Interest ◽  
Computation Time ◽  
Support Vector ◽  
Machine Model ◽  
Remote Sensing Imagery ◽  
Remote Sensing Classification

Advancements in remote sensing technology have led to improvements in the acquisition of land cover information. The extraction of accurate and timely knowledge about land cover from remote sensing imagery largely depends on the classification techniques used. Support vector machine has been receiving considerable attention as a promising method for classifying remote sensing imagery. However, the support vector machine learning process typically requires a large memory and significant computation time for treating a large sample set, in which some of the samples might be redundant and useless for the support vector machine model training. Therefore, higher-quality and fewer samples from the sample selection should be utilized for support vector machine-based remote sensing classification. A convex theory-based remote sensing sample selection algorithm for support vector machine classifiers is developed in this work. A Landsat-5 Thematic Mapper imagery acquired on August 31, 2009 (orbit number 113/27) is adopted in our experiments. The study area's land cover/use was divided into five categories. Using the region of interest tool, we select samples from the image of the study area, with each category consisting of 1000 independent pixels. Results show that for most cases, our method can achieve higher classification accuracy than random sample selection method.

Research on Quality Evaluation of Online Reservation Hotel APP Based on a RBF Neural Network and Support Vector Machine

2020 ◽  
Vol 12 (2) ◽  
pp. 50-64
Author(s):  
Ma Xiang
Keyword(s):  
Neural Network ◽  
Support Vector Machine ◽  
Rbf Neural Network ◽  
Evaluation Index System ◽  
Support Vector ◽  
Training Algorithm ◽  
Basic Model ◽  
Expert Interview ◽  
Online Reservation

In order to evaluate the quality of online reservation hotel APP, RBF neural and support vector machine are used to evaluate the quality of online reservation hotel APP. First, the basic theory of the RBF neural network is studied, and the training algorithm of the RBF neural network is designed. Second, the basic model of support vector machine is analyzed, and the training algorithm is designed. Third, the evaluation index system of online reservation hotel APP is designed, and the weight of every index is established based on questionnaires and expert interview, and the evaluation simulation is carried out for 25 online reservation hotel APP, results show that the RBF neural network and support vector machine can obtain consistent evaluation results, and the support vector machine has better evaluation performance.

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