Feed-forward Neural Networks (FNN) and Support Vector Machines (SVM) are two machine learning frameworks developed from very di:erent starting points of view. In this work a new learning model for FNN is proposed such that, in the linearly separable case, it tends to obtain the same solution as SVM. The key idea of the model is a weighting of the sum-of-squares error function, which is inspired...

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...

In the paper, we present a integrated approach combined Rough Set theory and SVM algorithm. The approach udl be divided into two steps. The fust step is classified roughlv with Rough Set, rule should be induced in this step by infonilation system. The second step should ht: classified precisely based on SVM Algorithn~, in this step we present two new fiuidrunental principles to help us select b...

SVM wrapper feature selection method for the classification problem, introduced in our previous work [1], is analyzed. The method based on modification of the standard SVM criterion by adding to the basic objective function a third term, which directly penalizes a chosen set of variables. The criterion divides the set of all variables into three subsets: deleted, selected and weighted features....

In this paper we solve a document classification task by incorporating prior/domain knowledge onto the SVM. The algorithm consists in to learn a prior classifier in the primal space (words) from an ‘external’ source of information to the text classification itself: patterns of reader’s eyes movements when reading relevant words for discriminating texts. This prior weight vector is then plugged ...

We propose to speed up the training process of support vector machines (SVM) by resorting to an approximate SVM, where a small number of representatives are extracted from the original training data set and used for training. Theoretical studies show that, in order for the approximate SVM to be similar to the exact SVM given by the original training data set, kernel k-means should be used to ex...

Training SVM requires large memory and long cpu time when the pattern set is large. To alleviate the computational burden in SVM training, we propose a fast preprocessing algorithm which selects only the patterns near the decision boundary. The time complexity of the proposed algorithm is much smaller than that of the naive M algorithm