We extend multiclass SVM to multiple prototypes per class. For this framework, we give a compact constrained quadratic problem and we suggest an efficient algorithm for its optimization that guarantees a local minimum of the objective function. An annealed process is also proposed that helps to escape from local minima. Finally, we report experiments where the performance obtained using linear ...

Even the support vector machine (SVM) has been proposed to provide a good generalization performance, the classi6cation result of the practically implemented SVM is often far from the theoretically expected level because their implementations are based on the approximated algorithms due to the high complexity of time and space. To improve the limited classi6cation performance of the real SVM, w...

Abstract This paper describes a new machine learning algorithm for regression and dimensionality reduction tasks. The Neural Support Vector Machine (NSVM) is a hybrid learning algorithm consisting of neural networks and support vector machines (SVMs). The output of the NSVM is given by SVMs that take a central feature layer as their input. The feature-layer representation is the output of a num...

The Support Vector Machine (SVM) has been extended to build up nonlinear classifiers using the kernel trick [1– 3]. As a learning model, it has the best recognition performance among the many methods currently known because it is devised to obtain high performance for unlearned data. The SVM uses linear threshold elements to build up two-classes classifier. It learns linear threshold element pa...

Classification is a fundamental problem at the intersection of machine learning and statistics. Machine learning methods have enjoyed considerable empirical success. However, they often have an ad hoc quality. It is desirable to have hard theoretical results which might highlight specific quantitative advantages of these methods. The statistical methods often tackle the classification problem t...

Using a recently introduced proximal support vector machine classifier [4], a very fast and simple incremental support vector machine (SVM) classifier is proposed which is capable of modifying an existing linear classifier by both retiring old data and adding new data. A very important feature of the proposed single-pass algorithm , which allows it to handle massive datasets, is that huge block...

Support vector machine is a popular method in machine learning. Incremental support vector machine algorithm is ideal selection in the face of large learning data set. In this paper a new incremental support vector machine learning algorithm is proposed to improve efficiency of large scale data processing. The model of this incremental learning algorithm is similar to the standard support vecto...

An important factor that influences the performance of support vector machine is how to select its parameters. In traditional C-support vector machine, it is difficult to select penalty parameter C and kernel parameters, inappropriate choice of those values may cause deterioration of its performance and increase algorithm complexity. In order to solve those problems, in this paper, selected v s...

SVMs suffer from the problem of large memory requirement and CPU time when trained in batch mode on large data sets. We overcome these limitations, and at the same time make SVMs suitable for learning with data streams, by constructing incremental learning algorithms. We first introduce and compare different incremental learning techniques, and show that they are capable of producing performanc...