Pattern Recognition Algorithms for Data Mining covers the topic of data mining from a pattern recognition perspective. This unique book presents real life data sets from various domains, such as geographic information systems, remote sensing imagery, and population census, to demonstrate the use of innovative new methodologies. Classical approaches are covered along with granular computation by integrating fuzzy sets, artificial neural networks, and genetic algorithms for efficient knowledge discovery. The authors then compare the granular computing and rough fuzzy approaches with the more...

CSCR, which is the ?rst nationalcenter in the countryin this domain, has many important objectives including distance learning, establishing linkage to premier institutes/industries, organizing specialized courses, as well as conducting f- damental research. The conference proceedings of PReMI-05, containing rigorouslyreviewed - pers, is published by Springer in its prestigious Lecture Notes in Computer S- ence (LNCS) series. Di?erent professional sponsors and funding agencies (both national and international) came forward to support this event for its success. These include, International...

This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. It examines how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries. Coverage also demonstrates the effectiveness of the genetic classifiers vis-a-vis several widely used classifiers, including neural networks.

This volume, containing contributions by experts from all over the world, is a collection of 21 articles which present review and research material describing the evolution and recent developments of various pattern recognition methodologies, ranging from statistical, syntactic/linguistic, fuzzy-set-theoretic, neural, genetic-algorithmic and rough-set-theoretic to hybrid soft computing, with significant real-life applications. In addition, the book describes efficient soft machine learning algorithms for data mining and knowledge discovery. With a balanced mixture of theory, algorithms and...

This collection of 21 articles contains new material and describes, in a unified way with extensive real life applications, the merits and significance of performing different image processing/analysis tasks in soft computing paradigm.

This book uses tutorials and new material to describe the basic concepts of soft-computing which potentially can be used in real-life sensor network applications. It is organized in a manner that exemplifies the use of an assortment of soft-computing applications for solving different problems in sensor networking. Written by worldwide experts, the chapters provide a balanced mixture of different problems concerning channel access, routing, coverage, localization, lifetime maximization and target tracking using emerging soft-computing applications.

A collection of 16 articles containing reviews and material. They describe the development of theories and methodologies in pattern recognition, image processing and vision using fuzzy logic, artificial neural networks, genetic algorithms, rough sets and wavelets, with real life applications.