its generalization to Tsallis case (2009)
Ambedkar Dukkipati, M. Narasimha Murty, Shalabh Bhatnagar
Information theoretic justification ofBoltzmann selection and
Ambedkar Dukkipati, Shalabh Bhatnagar, M. Narasimha Murty
Shannon entropy of a probability measure P, defined as − � dP dP X dµ ln dµ dµ on a measure space (X,M,µ), is not a natural extension from the discrete case. However, maximum entropy (ME)...
Quotient evolutionary space: Abstraction of evolutionary process w.r.t macroscopic properties (2008)
Amhedkar Dukkipati, M. Narasimha Murty, Shalahh Bhatnagar
Abstract- Darwinian evolution, which is character-ized in terms of particular macroscopic behavior that emerges from microscopic organismic interaction, con-siders populations as units of...
Gelfand-Yaglom-Perez Theorem for Generalized Relative Entropy Functionals (2008)
Ambedkar Dukkipati, Shalabh Bhatnagar, M. Narasimha Murty
The measure-theoretic definition of Kullback-Leibler relative-entropy (or simply KLentropy) plays a basic role in defining various classical information measures on general spaces. Entropy, mutual...
Quotient evolutionary space: Abstraction of evolutionary process w.r.t macroscopic properties (2008)
Ambedkar Dukkipati, M. Narasimha Murty, Shalabh Bhatnagar
Abstract- Darwinian evolution, which is characterized in terms of particular macroscopic behavior that emerges from microscopic organismic interaction, considers populations as units of evolutionary...
Selection by Parts: ‘Selection in Two Episodes’ in Evolutionary Algorithms (2008)
Ambedkar Duklupati, M. Narasimha Murty
Abstract-Natural selection is the central concept of Dar-winian evolution and hence selection is central for evolutionary computation. Naive models of evolution define natural selection as a process...
Use of Multi-category Proximal SVM for Data Set Reduction (2008)
Abstract. We present a tutorial introduction to Support Vector Machines (SVM) and try to show, using intuitive arguments, why SVM’s tend to perform so well on a variety of challenging problems. We...
Clustering Large Symbolic Datasets (2008)
M. Narasimha Murty, T. Ravindra Babu, V. K. Agrawal
Clustering is the process of partitioning a set of labeled/unlabeled patterns into meaningful groups so that patterns in each group/cluster are similar to each other in some sense and patterns in...
Use of Multi-category Proximal SVM for Data Set Reduction (2008)
Abstract. We present a tutorial introduction to Support Vector Machines (SVM) and try to show using intuitive arguments as to why SVM’s tend to perform so well on a variety of challenging problems....
Alexander J. Smola, M. Narasimha Murty
We present a fast iterative support vector training algorithm for a large variety of different formulations. It works by incrementally changing a candidate support vector set using a greedy approach,...
Multiclass Core Vector Machine (2007)
S. Asharaf, M. Narasimha Murty, S. K. Shevade
Even though several techniques have been proposed in the literature for achieving multiclass classification using Support Vector Machine(SVM), the scalability aspect of these approaches to handle...
Ambedkar Dukkipati, M. Narasimha Murty, Shalabh Bhatnagar
Abstract- A generalized evolutionary algorithm based on Tsallis statistics is proposed. The algorithm uses Tsallis generalized canonical distribution, which is one parameter generalization of...
Ambedkar Dukkipati, M. Narasimha Murty, Shalabh Bhatnagar
Abstract — Boltzmann selection is an important selection mechanism in evolutionary algorithms as it has theoretical properties which help in theoretical analysis. However, Boltzmann selection is...
An incremental prototype set building technique (2002)
V. Susheela Devi, M. Narasimha Murty
This paper deals with the task of "nding a set of prototypes from the training set. A reduced set is obtained which is used instead of the training set when nearest neighbour...
Selection by parts: `selection in two episodes' in evolutionary algorithms (2002)
Ambedkar Dukkipati, M. Narasimha Murty
Abstract- Natural selection is the central concept of Darwinian evolution and hence selection is central for evolutionary computation. Naive models of evolution define natural selection as a process...
A knowledge-based clustering scheme (1987)
Shekar, B, Murty, M Narasimha, Krishna, G
In this paper the notion of conceptual cohesiveness is precised and used to group objects semantically, based on a knowledge structure called ‘cohesion forest’. A set of axioms is proposed which...
A hybrid clustering procedure for concentric and chain-like clusters (1981)
Murty, M Narasimha, Krishna, G
K-means algorithm is a well known nonhierarchical method for clustering data. The most important limitations of this algorithm are that: (1) it gives final clusters on the basis of the cluster...