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...

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 ...

A large number of experimental data shows that Support Vector Machine (SVM) algorithm has obvious a large advantages in text classification, handwriting recognition, image classification, bioinformatics, and some other fields. To some degree, the optimization of SVM depends on its kernel function and Slack variable, the determinant of which is its parameters δ and c in the classification functi...

Support vector machines (SVM) are widely applied to various classification problems. However, most SVM need lengthy computation time when faced with a large and complicated dataset. This research develops a clustering algorithm for efficient learning. The method mainly categorizes data into clusters, and finds critical data in clusters as a substitute for the original data to reduce the computa...

Rough set theory is introduced into linear υ support vector machine (svm), and rough marginbased linear υ svm is proposed in this paper. By constructing rough lower margin, rough upper margin and rough boundary in linear υ svm, then we maximize the rough margin not margin in linear υ svm. Thus more points are considered in constructing the separating hyper-plane than those used in linear υ svm....

A new incremental support vector machine (SVM) algorithm is proposed which is based on multiple kernel learning. Through introducing multiple kernel learning into the SVM incremental learning, large scale data set learning problem can be solved effectively. Furthermore, different punishments are adopted in allusion to the training subset and the acquired support vectors, which may help to impro...

SVM algorithm has a great advantage when it deals with small sample data set. However, In the process of large sample data set classification, it always has to face to the problems of slowly learning and large storage space. This paper puts forward the process of space edge detection, designs and implements the space edge detection based SVM algorithm. The result of simulation experiments shows...