The problem of feature selection is a difficult combinatorial task in Machine Learning and of high practical relevance, e.g. in bioinformatics. Genetic Algorithms (GAs) offer a natural way to solve this problem. In this paper we present a special Genetic Algorithm, which especially takes into account the existing bounds on the generalization error for Support Vector Machines (SVMs). This new ap...

A relevance feedback (RF) approach for content-based image retrieval (CBIR) is proposed, which is based on Support Vector Machines (SVMs) and uses a feature selection technique to reduce the dimensionality of the image feature space. Specifically, each image is described by a multidimensional vector combining color, texture and shape information. In each RF round, the positive and negative exam...

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...

dna sequence, containing all genetic traits is not a functional entity. instead, it transfers to protein sequences by transcription and translation processes. this protein sequence takes on a 3d structure later, which is a functional unit and can manage biological interactions using the information encoded in dna. every life process one can figure is undertaken by proteins with specific functio...

Support Vector Machines (SVMs) were applied to interactive document retrieval that uses active learning. In such a retrieval system, the degree of relevance is evaluated by using a signed distance from the optimal hyperplane. It is not clear, however, how the signed distance in SVMs has characteristics of vector space model. We therefore formulated the degree of relevance by using the signed di...

We present an efficient coreset construction algorithm for large-scale Support Vector Machine (SVM) training in Big Data and streaming applications. A is a small, representative subset of the original data points such that model trained on provably competitive with set. Since size generally much smaller than set, our preprocess-then-train scheme has potential to lead significant speedups when S...

Many problems in classification involve huge numbers of irrelevant features. Variable selection reveals the crucial features, reduces dimensionality feature space, and improves model interpretation. In support vector machine literature, variable is achieved by l1 penalties. These convex relaxations seriously bias parameter estimates toward 0 tend to admit too many The current article presents a...