We introduce a new family of positive-definite kernel functions that mimic the computation in large, multilayer neural nets. These kernel functions can be used in shallow architectures, such as support vector machines (SVMs), or in deep kernel-based architectures that we call multilayer kernel machines (MKMs). We evaluate SVMs and MKMs with these kernel functions on problems designed to illustr...

We present a comparative study between two machine learning methods, Conditional Random Fields and Support Vector Machines for clinical named entity recognition. We explore their applicability to clinical domain. Evaluation against a set of gold standard named entities shows that CRFs outperform SVMs. The best F-score with CRFs is 0.86 and for the SVMs is 0.64 as compared to a baseline of 0.60.

One of the most powerful, popular and accurate classification techniques is support vector machines (SVMs). In this work, we want to evaluate whether the accuracy of SVMs can be further improved using training set selection (TSS), where only a subset of training instances is used to build the SVM model. By ccepted 3 September 2015 vailable online 30 September 2015

In this chapter, we use support vector machines (SVMs) to deal with two bioinformatics problems, i.e., cancer diagnosis based on gene expression data and protein secondary structure prediction (PSSP). For the problem of cancer diagnosis, the SVMs that we used achieved highly accurate results with fewer genes compared to previously proposed approaches. For the problem of PSSP, the SVMs achieved ...

In this paper, a combination of genetic algorithms and support vector machines (SVMs) is proposed. SVMs are used for solving classification tasks, whereas genetic algorithms are optimization heuristics combining direct and stochastic search within a solution space. Here, the solution space is formed by combinations of different SVM’s kernel functions and kernel parameters. We investigate classi...

We apply Support Vector Machines (SVMs) to identify English base phrases (chunks). SVMs are known to achieve high generalization performance even with input data of high dimensional feature spaces. Furthermore, by the Kernel principle, SVMs can carry out training with smaller computational overhead independent of their dimensionality. We apply weighted voting of 8 SVMsbased systems trained with...

In this paper, we present a comparative study on applications of Neuro Fuzzy and Support Vector Machines (SVMs) for pattern recognition. Since SVMs show good generalization performance on many real-life data and the approach is properly motivated theoretically, it has been applied to wide range of applications. This paper describes a brief introduction of SVMs and summarizes its numerous applic...

In this paper, we model large support vector machines (SVMs) by smaller networks in order to decrease the computational cost. The key idea is to generate additional training patterns using a trained SVM and use these additional patterns along with the original training patterns to train a neural network. Results verify the validity of the technique. Introduction A key element in a pattern recog...

Recently Support Vector Machines (SVMs) have played a leading role in pattern classification. SVMs are quite effective to classify static data in numerous applications. However, the use of SVMs in dynamically data driven application systems (DDDAS) is somewhat limited. This motivates the development of incremental approaches to handle DDDAS. In an incremental learning approach, it is critical t...

Support vector machines (SVMs) are theoretically well-justified machine learning techniques, which have also been successfully applied to many real-world domains. The use of optimization methodologies plays a central role in finding solutions of SVMs. This paper reviews representative and state-of-the-art techniques for optimizing the training of SVMs, especially SVMs for classification. The ob...