Nowadays, support vector machines (SVM) are receiving increasing attention in land cover/use classification although one of the major drawbacks of the technique is the kernel function selection and its parameters setting. In this paper, a novel SVM parameters optimization method based on selfadaptive mutation particle swarm optimizer (SAMPSO-SVM) is proposed to improve the generalization perfor...

alzheimer disease is one form of dementia in old age. alzheimer disease, the incurable disease, which is usually in the seventh decade of human life, shows its symptoms. the disease may be present for years without clinical symptoms. the current study identified the genes with altered expression in patients with alzheimer disease. the important sequence of each gene in alzheimer disease was fou...

with the explosive growth in amount of information, it is highly required to utilize tools and methods in order to search, filter and manage resources. one of the major problems in text classification relates to the high dimensional feature spaces. therefore, the main goal of text classification is to reduce the dimensionality of features space. there are many feature selection methods. however...

Multi-sensor data fusion of E-tongue and E-nose can provide a more comprehensive and more accurate analysis results. However, it also brings some redundant information, it is a hot issue to reduce the feature dimension for pattern recognition. In this paper, the taste-olfactory data fusion based on E-tongue and E-nose combined with Support Vector Machine (SVM) was used to classify five differen...

The support vector machine (SVM) has been receiving increasing interest in an area of QSAR study for its ability in function approximation and remarkable generalization performance. However, selection of support vectors and intensive optimization of kernel width of a nonlinear SVM are inclined to get trapped into local optima, leading to an increased risk of underfitting or overfitting. To over...

The ν-support vector machine (ν-SVM) for classification proposed by Schölkopf et al. has the advantage of using a parameter ν on controlling the number of support vectors. In this paper, 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 on solv...

In this paper, we consider the distributed version of Support Vector Machine (SVM) under the coordinator model, where all input data (i.e., points in R space) of SVM are arbitrarily distributed among k nodes in some network with a coordinator which can communicate with all nodes. We investigate two variants of this problem, with and without outliers. For distributed SVM without outliers, we pro...

We propose a novel approach that reduces cost-sensitive classification to one-sided regression. The approach stores the cost information in the regression labels and encodes the minimum-cost prediction with the onesided loss. The simple approach is accompanied by a solid theoretical guarantee of error transformation, and can be used to cast any one-sided regression method as a costsensitive cla...

The new parallel incremental Support VectorMachine (SVM) algorithm aims at classifying very large datasets on graphics processing units (GPUs). SVM and kernel related methods have shown to build accurate models but the learning task usually needs a quadratic programming, so that the learning task for large datasets requires big memory capacity and a long time. We extend the recent finite Newton...

Since the aerobics is introduced into the college and university, it becomes popular in teachers and students. In order to develop the aerobics better and improve the level of the aerobics, it is necessary to predict the aerobics performance. Support vector machine method is one of the frequently-used prediction methods. In order to improve the performance of traditional LS-SVM, we put forward ...