Orazio Giustolisi Engineering Faculty of Taranto, Technical University of Bari, via Turismo no 8, Paolo VI, 74100 Taranto, Italy E-mail: [email protected]; [email protected] Support Vector Machines are kernel machines useful for classification and regression problems. In this paper, they are used for non-linear regression of environmental data. From a structural point of view, Sup...

This paper presents a new method for selecting valuable training data for support vector machines (SVM) from large, noisy sets using a genetic algorithm (GA). SVM training data selection is a known, however not extensively investigated problem. The existing methods rely mainly on analyzing the geometric properties of the data or adapt a randomized selection, and to the best of our knowledge, GA...

An accurate reservoir characterization is a crucial task for the development of quantitative geological models and reservoir simulation. In the present research work, a novel view is presented on the reservoir characterization using the advantages of thin section image analysis and intelligent classification algorithms. The proposed methodology comprises three main steps. First, four classes of...

We use a combination of linear support vector machines and hidden markov models for dialog act tagging in the HCRC MapTask corpus, and obtain better results than those previously reported. Support vector machines allow easy integration of sparse highdimensional text features and dense low-dimensional acoustic features, and produce posterior probabilities usable by sequence labelling algorithms....

In this paper, we propose a new algorithm called multiple physicochemical properties and support vector machines (MppS) which uses support vector machines (SVM) in conjunction with multiple physicochemical properties of amino acids. The algorithm was tested in two problems: HIV-protease and recognition of T-cell epitopes. A series of SVM classifiers combined with the ‘‘max rule’’ enables us to ...

This paper describes a comparison of approaches for time series classification. Our comparisons included two different outlier removal methods (discords and reverse nearest neighbor), two different distance measures (Euclidean distance and dynamic time warping), and two different classification algorithms (k nearest neighbor and support vector machines). An algorithm for semi-supervised learnin...

Currently the best algorithms for transcription factor binding site prediction are severely limited in accuracy. There is good reason to believe that predictions from these different classes of algorithms could be used in conjunction to improve the quality of predictions. In this paper, we apply single layer networks, rules sets and support vector machines on predictions from 12 key algorithms....

In this paper, we present our approach to SemEval-2013 Task 9.2. It is a feature rich classification using LIBSVM for Drug-Drug Interactions detection in the BioMedical domain. The features are extracted considering morphosyntactic, lexical and semantic concepts. Tools like openDMAP and TEES are used to extract semantic concepts from the corpus. The best F-score that we got for DrugDrug Interac...