1 Introduction In this paper, we explore the use of Support Vector Machines (SVMs) for CoNLL-2000 shared task, chunk identification. SVMs are so-called large margin classifiers and are well-known as their good generalization performance. We investigate how SVMs with a very large number of features perform with the classification task of chunk labelling.

Support vector machines (SVMs) and fuzzy rule systems are functionally equivalent under some conditions. Therefore, the learning algorithms developed in the field of support vector machines can be used to adapt the parameters of fuzzy systems. Extracting fuzzy models from support vector machines has the inherent advantage that the model does not need to determine the number of rules in advance....

In this paper, different types of learning networks, such as artificial neural networks (ANNs), Bayesian neural networks (BNNs), support vector machines (SVMs) and Bayesian support vector machines (BSVMs) are applied for tornado forecasting. The last two approaches utilize kernel methods to address nonlinearity of the data in the input space. All methods are applied to forecast when tornadoes o...

This paper explores scalability issues associated with the Named Entity Recognition problem in the biomedical publications domain using Support Vector Machines. The performance results using existing binary and multi-class SVMs with increasing training data are compared to results obtained using our new implementations. Our approach eliminates prior language or domain-specific knowledge and ach...

In the past decade, support vector machines (SVMs) have gained the attention of many researchers. SVMs are non-parametric supervised learning schemes that rely on statistical learning theory which enables learning machines to generalize well to unseen data. SVMs refer to kernel-based methods that have been introduced as a robust approach to classification and regression problems, lately has han...

This paper explores the use of Support Vector Machines (SVMs) for learning text classiers from examples. It analyzes the particular properties of learning with text data and identi es, why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety o...

Least Squares Support Vector Machines (LS-SVMs) were proposed by replacing the inequality constraints inherent to L1-SVMs with equality constraints. So far this idea has only been suggested for a least squares (L2) loss. We describe how this can also be done for the sumof-slacks (L1) loss, yielding a new classifier (Least 1-Norm SVMs) which gives similar models in terms of complexity and accura...

This paper explores the use of Support Vector Machines (SVMs) for learning text classiers from examples. It analyzes the particular properties of learning with text data and identi es, why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety o...

This paper explores the use of Support Vector Machines (SVMs) for learning text classiers from examples. It analyzes the particular properties of learning with text data and identi es, why SVMs are appropriate for this task. Empirical results support the theoretical ndings. SVMs achieve substantial improvements over the currently best performing methods and they behave robustly over a variety o...

We re-visit Support Vector Machines (SVMs) and provide a novel interpretation thereof in terms of weighted class means and scatter theory. The gained theoretical insight can be translated into a highly efficient extension to multi-class SVMs: mScatter-SVMs. Numerical simulations reveal that more than an order of magnitude speed-up can be gained while the classification performance remains large...