G. Phanendra Babu

Indian Institute of Science Bangalore

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M. Narasimha Murty

Indian Institute of Science Bangalore

S. Sathiya Keerthi

Criteo (France)

G. Prem Kumar

Indian Institute of Science Bangalore

MN Murty

Institute of Economic Growth

Mohan Kankanhalli

National University of Singapore

B.R. Ram

B. M. Mehtre

Institute for Development and Research in Banking Technology

Pawan Kumar

Visvesvaraya Technological University

Narasimha M. Murty

Indian Institute of Science Bangalore

M. Ramakrishna Murty

Indian Institute of Management Visakhapatnam

Cooperative Institutions

Indian Institute of Science Bangalore

National University of Singapore

Criteo (France)

Microsoft (United States)

Tecolote Research (United States)

Yahoo (United Kingdom)

Yahoo (United States)

MediaTek (China)

Los Angeles Mission College

Indian Institute of Technology Madras

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Optimal thresholding using multi-state stochastic connectionist approach

Pattern Recognition Letters (1995)

G. Phanendra Babu M. Narasimha Murty

Connectionism

Pascal (unit)

10.1016/0167-8655(94)00062-8

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Citations (4)

Controlled offspring generation in evolutionary programming

G. Phanendra Babu MN Murty

The paper presents an extension to the standard evolutionary programming (SEP) technique, which incorporates the concept of fitness based offspring generation. Each parent in the population tries to solve the problem of surviving into future generations by the controlled generation of offspring. Offspring generated by parents with good environmental support have a high chance of survival as compared to the offspring generated by other parents. It is argued that generating more offspring from a low fit parent yields a high chance for the survival of its progeny. As a consequence, a modification is required to the SEP to generate more offspring than the number of parents. Experimental results for the selected problems are compared with the results obtained by the SEP technique

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Simulated annealing for selecting optimal initial seeds in the K-means algorithm

G. Phanendra Babu M. Ramakrishna Murty

Explores the applicability of simulated annealing, a probabilistic search method, for finding optimal partition of the data. A new formulation of the clustering problem is investigated. In order to obtain an optimal partition, a search is undertaken to locate optimal initial seeds, such that the K-means algorithm converges to optimal partition. Search space involved in this process is continuous, so discretization is done and simulated annealing is employed for locating optimal initial seeds. Experimental results substantiate the proposed method. Results obtained with the selected data sets are presented.

Adaptive simulated annealing

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Connectionist approach for clustering

G. Phanendra Babu M. Narasimha Murty

This paper presents a stochastic connectionist approach for cluster analysis. Clustering problem is formulated as a real-parameter function optimization problem and is solved using the proposed approach. As the proposed connectionist approach performs stochastic search, it avoids getting stuck in a local minimum, and guarantees asymptotic convergence to optimal solution. The amenability of connectionist approaches to massive parallelization enables one to obtain linear speedup with available parallel hardware. Several data sets are clustered using the proposed approach and the partitions obtained are (near) optimal in nature. Results pertaining to some important data sets are presented.< >

Connectionism

10.1109/icnn.1994.375028

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Citations (2)

Clustering with evolution strategies

Pattern Recognition (1994)

G. Phanendra Babu M. Narasimha Murty

Centroid

10.1016/0031-3203(94)90063-9

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Citations (135)

Self-organizing neural networks for spatial data

Pattern Recognition Letters (1997)

G. Phanendra Babu

Self-organizing map

Rectangle

Object-based spatial database

Data point

Linear space

Bounding overwatch

10.1016/s0167-8655(97)00003-2

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A stochastic connectionist approach for global optimization with application to pattern clustering

IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) (2000)

G. Phanendra Babu Narasimha M. Murty S. Sathiya Keerthi

In this paper, a stochastic connectionist approach is proposed for solving function optimization problems with real-valued parameters. With the assumption of increased processing capability of a node in the connectionist network, we show how a broader class of problems can be solved. As the proposed approach is a stochastic search technique, it avoids getting stuck in local optima. Robustness of the approach is demonstrated on several multi-modal functions with different numbers of variables. Optimization of a well-known partitional clustering criterion, the squared-error criterion (SEC), is formulated as a function optimization problem and is solved using the proposed approach. This approach is used to cluster selected data sets and the results obtained are compared with that of the K-means algorithm and a simulated annealing (SA) approach. The amenability of the connectionist approach to parallelization enables effective use of parallel hardware.

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Robustness

10.1109/3477.826943

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Citations (15)

Clan-based evolutionary approach for solving control problems

Computers & Mathematics with Applications (1996)

G. Phanendra Babu M. Narasimha Murty B.R. Ram

Clan

10.1016/0898-1221(96)00005-3

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Citations (1)