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

In this chapter, we elaborate on the well-known relationship between Gaussian Processes (GP) and Support Vector Machines (SVM). Secondly, we present approximate solutions for two computational problems arising in GP and SVM. The rst one is the calculation of the posterior mean for GP classiiers using a `naive' mean eld approach. The second one is a leave-one-out estimator for the generalization...

A new procedure for learning cost-sensitive SVM(CS-SVM) classifiers is proposed. The SVM hinge loss is extended to the cost sensitive setting, and the CS-SVM is derived as the minimizer of the associated risk. The extension of the hinge loss draws on recent connections between risk minimization and probability elicitation. These connections are generalized to cost-sensitive classification, in a...

Support vector machine (SVM) is one of the most effective classification methods for cancer detection. The efficiency and quality of a SVM classifier depends strongly on several important features and a set of proper parameters. Here, a series of classification analyses, with one set of photoacoustic data from ovarian tissues ex vivo and a widely used breast cancer dataset- the Wisconsin Diagno...

The agricultural data classification is a hot topic in the field of precision agriculture. Support vector machine (SVM) is a kind of structural risk minimization based learning algorithms. As a popular machine learning algorithm, SVM has been widely used in many fields such as information retrieval and text classification in the last decade. In this paper, SVM is introduced to classify the agri...

Ranking algorithms are often introduced with the aim of automatically personalising search results. However, most ranking algorithms developed in the machine learning community rely on a careful choice of some regularisation parameter. Building upon work on the regularisation path for kernel methods, we propose a parameter selection algorithm for ranking SVM. Empirical results are promising.