Large-scale classiication is a very active research line in data mining. It can be applied to problems like credit card fraud detection or content-based document browsing. In recent years, several eecient algorithms for this area have been proposed by Mangasarian and Musicant. These approaches, based on quadratic problems, are: Successive OverRelaxation (SOR), Active Support Vector Machines (AS...

Feature extraction and classification are two key steps for activity recognition in a smart home environment. In this work, we used three methods for feature extraction: Principal Component Analysis (PCA), Independent Component Analysis (ICA), and Linear Discriminant Analysis (LDA). The new features selected by each method are then used as the inputs for a Weighted Support Vector Machines (WSVM...

This paper gives a novel method for improving classification performance for cancer classification with very few microarray Gene expression data. The method employs classification with individual gene ranking and gene subset ranking. For selection and classification, the proposed method uses the same classifier. The method is applied to three publicly available cancer gene expression datasets f...

Predicting protein-protein interactions represent a key step in understanding proteins functions. This is due to the fact that proteins usually work in context of other proteins and rarely function alone. Machine learning techniques have been applied to predict protein-protein interactions. However, most of these techniques address this problem as a binary classification problem. Although it is...

We present new unsupervised and semi-supervised training algorithms for multi-class support vector machines based on semidefinite programming. Although support vector machines (SVMs) have been a dominant machine learning technique for the past decade, they have generally been applied to supervised learning problems. Developing unsupervised extensions to SVMs has in fact proved to be difficult. ...

Support Vector Machines, one of the new techniques for pattern classification, have been widely used in many application areas. The kernel parameters setting for SVM in a training process impacts on the classification accuracy. Feature selection is another factor that impacts classification accuracy. The objective of this research is to simultaneously optimize the parameters and feature subset ...

Gene expression data sets are used to classify and predict patient diagnostic categories. As we know, it is extremely difficult and expensive to obtain gene expression labelled examples. Moreover, conventional supervised approaches cannot function properly when labelled data (training examples) are insufficient using Support Vector Machines (SVM) algorithms. Therefore, in this paper, we suggest...

This paper proposes a hybrid learning method to speed up the classification procedure of Support Vector Machines (SVM). Comparing most algorithms trying to decrease the support vectors in an SVM classifier, we focus on reducing the data points that need SVM for classification, and reduce the number of support vectors for each SVM classification. The system uses a Nearest Neighbor Classifier (NN...

Support Vector Machines (SVM) provide an interesting computational paradigm for the classification of data from high energy physics and particle astrophysics experiments. In this study the classification power of support vector machines is compared with those from standard supervised algorithms, i.e. likelihood ratio (LR) and artificial neural networks (ANN), using test beam data from the trans...

⎯The use of support vector machines (SVM) for watermarking of 3D mesh models is investigated. SVMs have been widely explored for images, audio, and video watermarking but to date the potential of SVMs has not been explored in the 3D watermarking domain. The proposed approach utilizes SVM as a binary classifier for the selection of vertices for watermark embedding. The SVM is trained with featur...