objectives in order to increase the classification accuracy of airs, this study introduces a new hybrid system that incorporates a support vector machine into airs for diagnosing tuberculosis. background tuberculosis (tb) is a major global health problem, which has been ranked as the second leading cause of death from an infectious disease worldwide. diagnosis based on cultured specimens is the...

The major drawback of Support Vector Machines (SVMs) consists of the training time, which is at least quadratic to the number of data. Among the multitude of approaches developed to alleviate this limitation, several research works showed that mixtures of experts can drastically reduce the runtime of SVMs. The mixture employs a set of SVMs each of which is trained on a sub-set of the original d...

Support Vector Machines (SVMs) have become a popular tool for learning with large amounts of high dimensional data. However, it may sometimes be preferable to learn incrementally from previous SVM results, as computing a SVM is very costly in terms of time and memory consumption or because the SVM may be used in an online learning setting. In this paper an approach for incremental learning with...

With the advent of consumer generated media (e.g., Amazon reviews, Twitter, etc.), sentiment classification becomes a heated topic. Previous work heavily relies on a large amount of linguistic resources, which are difficult to obtain in resource-scarce languages. To overcome this problem, we investigate the usefulness of label propagation, which is a graph-based semi-supervised learning method....

Support vector machines (SVMs) have played a key role in broad classes of problems arising in various fields. Much more recently, SVMs have become the tool of choice for problems arising in data classification and mining. This paper emphasizes some recent developments that the author and his colleagues have contributed to such as: generalized SVMs (a very general mathematical programming framew...

We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HMSVMs). The HVS model is an extension of the basic discrete Markov model in which context is encoded as a stack-oriented state vector. The HM-SVMs combine the advantages of the hidden Markov models and the support vector ...

Medical image recognition is one of the most important unsolved problems in medicine. Taking the mammogram as the object for research, this paper proposes a method for mammographic image recognition using rough sets and support vector machines (SVMs). Firstly, reduce mammographic noise. Secondly, extract texture and shape features to consist of feature vector that can represent the mammogram ac...

The relation existing between support vector machines (SVMs) and recurrent associative memories is investigated. The design of associative memories based on the generalized brain-state-in-a-box (GBSB) neural model is formulated as a set of independent classification tasks, which can be efficiently solved by standard software packages for SVM learning. Some properties of the networks designed in...

In this paper we study nonlinear ARX models in relation to a class of kernel based models which make use of kernel induced feature spaces, a methodology which is common in the area of support vector machines (SVMs). Methods are proposed for extending the use of least squares support vector machine (LS-SVM) models towards a robust setting. In order to understand the robustness of these estimator...

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...