S
Sankar K. Pal
Researcher at Indian Statistical Institute
Publications - 455
Citations - 25108
Sankar K. Pal is an academic researcher from Indian Statistical Institute. The author has contributed to research in topics: Fuzzy logic & Fuzzy set. The author has an hindex of 70, co-authored 446 publications receiving 23727 citations. Previous affiliations of Sankar K. Pal include National Academy of Sciences & University of California, Santa Barbara.
Papers
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Journal ArticleDOI
A review on image segmentation techniques
Nikhil R. Pal,Sankar K. Pal +1 more
TL;DR: Attempts have been made to cover both fuzzy and non-fuzzy techniques including color image segmentation and neural network based approaches, which addresses the issue of quantitative evaluation of segmentation results.
Journal ArticleDOI
Unsupervised feature selection using feature similarity
TL;DR: An unsupervised feature selection algorithm suitable for data sets, large in both dimension and size, based on measuring similarity between features whereby redundancy therein is removed, which does not need any search and is fast.
Journal ArticleDOI
Multilayer perceptron, fuzzy sets, and classification
Sankar K. Pal,Sushmita Mitra +1 more
TL;DR: A fuzzy neural network model based on the multilayer perceptron, using the backpropagation algorithm, and capable of fuzzy classification of patterns is described, and the results are compared with those of the conventional MLP, the Bayes classifier, and other related models.
Fuzzy models for pattern recognition
James C. Bezdek,Sankar K. Pal +1 more
TL;DR: The basic structure of fuzzy sets theory as it applies to the major problems encountered in the design of a pattern recognition system is described.
Book
Rough-Fuzzy Hybridization: A New Trend in Decision Making
Sankar K. Pal,Andrzej Skowron +1 more
TL;DR: This volume provides a collection of twenty articles containing new material and describing the basic concepts and characterizing features of rough set theory and its integration with fuzzy set theory, for developing an efficient soft computing strategy of machine learning.