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This carefully edited book reflects and advances the state of the art in the area of Data Mining and Knowledge Discovery with Evolutionary Algorithms. It emphasizes the utility of different evolutionary computing tools to various facets of knowledge discovery from databases, ranging from theoretical analysis to real-life applications. Evolutionary Computation in Data Mining provides a balanced mixture of theory, algorithms and applications in a cohesive manner, and demonstrates how the different tools of evolutionary computation can be used for solving real-life problems in data mining and...

This book constitutes the refereed proceedings of the Second International Conference on Pattern Recognition and Machine Intelligence, PReMI 2007, held in Kolkata, India in December 2007. The papers are organized in topical sections on pattern recognition, image analysis, soft computing and applications, data mining and knowledge discovery, bioinformatics, signal and speech processing, document analysis and text mining, biometrics, and video analysis.

The term evolutionary computing (EC) refers to the study of the foundations and applications of certain heuristic techniques based on the principles of natural evolution, and thus the aim when designing evolutionary algorithms (EAs) is to mimic some of the processes taking place in natural evolution.

Many researchers around the world have been developing EC methodologies for designing intelligent decision-making systems for a variety of real-world problems. This book provides a collection of 40 articles, written by leading experts in the field, containing new material on both the...

The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.

The present volume provides a collection of seven articles containing new and high quality research results demonstrating the significance of Multi-objective Evolutionary Algorithms (MOEA) for data mining tasks in Knowledge Discovery from Databases (KDD). These articles are written by leading experts around the world. It is shown how the different MOEAs can be utilized, both in individual and integrated manner, in various ways to efficiently mine data from large databases.

This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.

This book presents some of the most recent research results in the area of machine learning and robot perception. The chapters represent new ways of solving real-world problems. The book covers topics such as intelligent object detection, foveated vision systems, online learning paradigms, reinforcement learning for a mobile robot, object tracking and motion estimation, 3D model construction, computer vision system and user modelling using dialogue strategies. This book will appeal to researchers, senior undergraduate/postgraduate students, application engineers and scientists.