This paper describes an approach for the problem of face pose discrimination using Support Vector Machines (SVM). Face pose discrimination means that one can label the face image as one of several known poses. Face images are drawn from the standard FERET data base. The training set consists of 150 images equally distributed among frontal, approximately 33.75 rotated left and right poses, respe...

Most face recognition systems assume that the geometry of the image formation process is frontal. If additional poses, beyond the frontal one, are possible, then it becomes necessary to estimate the actual imaging pose. Once a face is detected and its pose is estimated one proceeds by normalizing the face images to account for geometrical and illumination changes, possibly using information abo...

Supervised text categorisation is a significant tool considering the vast amount of structured, unstru ctured, or semi-structured texts that are available from internal or external enterprise resources. The goal of supervised text categorisation is to assign text documents to finite pre -specified categories in order to extract and automatically organise information coming from th ese resources...

We propose to solve a text categorization task using a new metric between documents, based on a priori semantic knowledge about words. This metric can be incorporated into the definition of radial basis kernels of Support Vector Machines or directly used in a K-nearest neighbors algorithm. Both SVM and KNN are tested and compared on the 20 newsgroups database. Support Vector Machines provide th...

We describe an iterative algorithm for building vector machines used in classification tasks. The algorithm builds on ideas from support vector machines, boosting, and generalized additive models. The algorithm can be used with various continuously differential functions that bound the discrete (0-1) classification loss and is very simple to implement. We test the proposed algorithm with two di...

In this paper we describe a new hybrid distributed/shared memory parallel software for support vector machine learning on large data sets. The support vector machine (SVM) method is a well-known and reliable machine learning technique for classification and regression tasks. Based on a recently developed shared memory decomposition algorithm for support vector machine classifier design we incre...

We consider the problem of how to design and implement communication-efficient versions of parallel support vector machines, a widely used classifier in statistical machine learning, for distributed memory clusters and supercomputers. The main computational bottleneck is the training phase, in which a statistical model is built from an input data set. Prior to our study, the parallel isoefficie...

This paper discusses about combining Support Vector Machine and decision trees for multi class text classification. Support Vector Machines are trained on each class at each level of the tree and the SVM which is more successful in predicting a class at that level is selected as the decision in that node. Thus a tree is constructed with different SVM in each node. And the tree constructed is us...

Support vector machines (SVM) have been promising methods for classification because of their solid mathematical foundations which convey several salient properties that other methods hardly provide. However, despite of the prominent properties of SVM, they are not as favored for large-scale data as complexity of SVM is highly dependent on the size of a data set. Microarray gene expression data...

In this paper we introduce the use of semi-supervised support vector machines for rainfall estimation using images obtained from visible and infrared NOAA satellite channels. Two experiments were performed, one involving traditional SVM and other using semi-supervised SVM (SVM). The SVM approach outperforms SVM in our experiments, with can be seen as a good methodology for rainfall satellite es...