Product Description: Data mining (DM) consists of extracting interesting knowledge from re- world, large & complex data sets; and is the core step of a broader process, called the knowledge discovery from databases (KDD) process. In addition to the DM step, which actually extracts knowledge from data, the KDD process includes several preprocessing (or data preparation) and post-processing (or knowledge refinement) steps...read more

Product Description: 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...read more

Product Description: This review volume provides from both theoretical and application points of views, recent developments and state-of-the-art reviews in various areas of pattern recognition, image processing, machine learning, soft computing, data mining and web intelligence...read more

Product Description: 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)...read more

Product Description: 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...read more

Product Description: This volume provides a collection of sixteen articles containing review and new material. In a unified way, they describe the recent 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 significant real life applications...read more

Any task that involves decision-making can benefit from soft computing techniques which allow premature decisions to be deferred. The processing and analysis of images is no exception to this rule. In the classical image analysis paradigm, the first step is nearly always some sort of segmentation process in which the image is divided into (hopefully, meaningful) parts. It was pointed out nearly 30 years ago by Prewitt (1] that the decisions involved in image segmentation could be postponed by regarding the image parts as fuzzy, rather than crisp, subsets of the image. It was also realized very early that many basic properties of and operations on image subsets could be extended to fuzzy subsets; for example, the classic paper on fuzzy sets by Zadeh [2] discussed the "set algebra" of fuzzy sets (using sup for union and inf for intersection), and extended the defmition of convexity to fuzzy sets. These and similar ideas allowed many of the methods of image analysis to be generalized to fuzzy image parts. For are cent review on geometric description of fuzzy sets see, e. g. , [3]. Fuzzy methods are also valuable in image processing and coding, where learning processes can be important in choosing the parameters of filters, quantizers, etc. (view table of contents)