A Classification EM algorithm for clustering and two stochastic versions
Computational Statistics & Data Analysis - Special issue on optimization techniques in statistics
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
Layered Learning in Multiagent Systems: A Winning Approach to Robotic Soccer
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
RSES and RSESlib - A Collection of Tools for Rough Set Computations
RSCTC '00 Revised Papers from the Second International Conference on Rough Sets and Current Trends in Computing
Adaptive Classifier Construction: An Approach to Handwritten Digit Recognition
TSCTC '02 Proceedings of the Third International Conference on Rough Sets and Current Trends in Computing
Feature Selection Using Multi-Objective Genetic Algorithms for Handwritten Digit Recognition
ICPR '02 Proceedings of the 16 th International Conference on Pattern Recognition (ICPR'02) Volume 1 - Volume 1
Rough Set Theory and Granular Computing
Rough Set Theory and Granular Computing
Handbook of Granular Computing
Handbook of Granular Computing
Fuzzy logic = computing with words
IEEE Transactions on Fuzzy Systems
A View on Rough Set Concept Approximations
Fundamenta Informaticae - The 9th International Conference on Rough Sets, Fuzzy Sets, Data Mining and Granular Conputing (RSFDGrC 2003)
Information Granule Decomposition
Fundamenta Informaticae - Concurrency Specification and Programming (CS&P'2000)
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This paper summarizes the some of the recent developments in the area of application of rough sets and granular computing in hierarchical learning. We present the general framework of rough set based hierarchical learning. In particular, we investigate several strategies of choosing the appropriate learning algorithms for first level concepts as well as the learning methods for the intermediate concepts. We also propose some techniques for embedding the domain knowledge into the granular, layered learning process in order to improve the quality of hierarchical classifiers. This idea, which has been envisioned and developed by professor Andrzej Skowron over the last 10 years, shows to be very efficient in many practical applications. Throughout the article, we illustrate the proposed methodology with three case studies in the area of pattern recognition. The studies demonstrate the viability of this approach for such problems as: sunspot classification, hand-written digit recognition, and car identification.