Aroor Dinesh Dileep

Learn More
Dynamic kernel (DK)-based support vector machines are used for the classification of varying length patterns. This paper explores the use of intermediate matching kernel (IMK) as a DK for classification of varying length patterns of long duration speech represented as sets of feature vectors. The main issue in construction of IMK is the choice for the set(More)
In this paper, we propose an approach for identification of encryption method for block ciphers using support vector machines. The task of identification of encryption method from cipher text only is considered as a document categorization task. We address the issues in representing a cipher text by a document vector. We consider the common dictionary based(More)
Multiple kernel learning (MKL) approach for selecting and combining different representations of a data is presented. Selection of features from a representation of data using the MKL approach is also addressed. A base kernel function is used for each representation as well as for each feature from a representation. A new kernel is obtained as a linear(More)
In this paper, we propose the example-specific density based matching kernel (ESDMK) for classification of scene images represented as sets of local feature vectors. The proposed kernel is computed between the pair of examples, represented as sets of local feature vectors, by matching the estimates of example-specific densities computed at every local(More)
Classification of long duration speech, represented as varying length sets of feature vectors using support vector machine (SVM) requires a suitable kernel. In this paper we propose a novel segment-level pyramid match kernel (SLPMK) for the classification of varying length patterns of long duration speech represented as sets of feature vectors. This kernel(More)