background: prediction of interaction sites within the membrane protein complexes using the sequence data is of a great importance, because it would find applications in modification of molecules transport through membrane, signaling pathways and drug targets of many diseases. nevertheless, it has gained little attention from the protein structural bioinformatics community. methods: in this stu...

We address the problems of sparse multiclass and multilabel classifier design and devise new algorithms using margin based ideas. Many online applications such as image classification or text categorization demand fast inference. State-of-the-art classifiers such as Support Vector Machines (SVM) are not preferred in such applications because of slow inference, which is mainly due to the large n...

In this paper, we introduce new Fuzzy Support Vector Machines (FSVMs) for a multiclass classification. The suggested Fuzzy Support Vector Machines include the data distribution with the density estimated in a set of functions defined as Gaussian mixture. The proposed method gives more appropriate boundaries than the classical FSVM method. We demonstrate some examples which confirm our approach.

In this paper we extend Support Vector Machines (SVM) for speaker independent Consonant – Vowel (CV) unit classification. Here we adopt the technique known as Decision Directed Acyclic Graph (DDAG) , which is used to combine many two class classifiers into multiclass classifier. Using Reconstructed State Space (RSS) based State Space Point Distribution (SSPD) parameters, we obtain an average sp...

This paper proposes a method to perform class-specific feature selection in multiclass support vector machines addressed with the one-against-all strategy. The main issue arises at the final step of the classification process, where binary classifier outputs must be compared one against another to elect the winning class. This comparison may be biased towards one specific class when the binary ...

various statistical methods have been proposed in terms of predicting the outcomes of facing special factors. in the classical approaches,  making the probability distribution or known probability density functions is ordinarily necessary to predict the desired outcome. however, most of the times enough information about the probability distribution of studied variables is not available to the ...

The increase in the availability of protein interaction studies in textual format coupled with the demand for easier access to the key results has lead to a need for text mining solutions. In the text processing pipeline, classification is a key step for extraction of small sections of relevant text. Consequently, for the task of locating protein-protein interaction sentences, we examine the us...