Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realise the system. A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subjective information. The other interesting aspect of the volume is that it covers all three major facets of soft computing: fuzzy logic, neural networks and evolutionary computing. Contents: Dimensionality Reduction Techniques for Interactive Visualization, Exploratory Data Analysis, and Classification (A KAnig); Feature Selection by Artificial Neural Network for Pattern Classification (B Chakraborty); A New Clustering with Estimation of Cluster Number Based on Genetic Algorithm (K Imai et al.); Minimizing the Measurement Cost in the Classification of New Samples by Neural-Network-Based Classifiers (H Ishibuchi & M Nii); Extraction of Fuzzy Rules from Numerical Data for Classifiers (N R Pal & A Sarkar); A Texture Image Segmentation Method Using Neural Networks and Binary Features (J Zhang & S Oe); Image Retrieval System Based on Subjective Information (K Yoshida et al.); and other papers. Readership: Graduate students, researchers and lecturers in pattern recognition and image analysis."

Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realise the system.A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subjective information. The other interesting aspect of the volume is that it covers all three major facets of soft computing: fuzzy logic, neural networks and evolutionary computing.

The neuro-fuzzy approach to pattern recognition-a unique overview Recent years have seen a surge of interest in neuro-fuzzy computing, which combines fuzzy logic, neural networks, and soft computing techniques. This book focuses on the application of this new tool to the rapidly evolving area of pattern recognition. Written by two leaders in neural networks and soft computing research, this landmark work presents a unified, comprehensive treatment of the state of the art in the field. The authors consolidate a wealth of information previously cattered in disparate articles, journals, and edited volumes, explaining both the theory of neuro-fuzzy computing and the latest methodologies for performing different pattern recognition tasks in the neuro-fuzzy network-classification, feature evaluation, rule generation, knowledge extraction, and hybridization. Special emphasis is given to the integration of neuro-fuzzy methods with rough sets and genetic algorithms (GAs) to ensure more efficient recognition systems. Clear, concise, and fully referenced, Neuro-Fuzzy Pattern Recognition features extensive examples and highlights key applications in speech, machine learning, medicine, and forensic science. It is an extremely useful resource for scientists and engineers in laboratories and industry as well as for anyone seeking up-to-date information on the advantages of neuro-fuzzy pattern recognition in new computer technologies.

Pattern recognition (PR) consists of three important tasks: feature analysis, clustering and classification. Image analysis can also be viewed as a PR task. Feature analysis is a very important step in designing any useful PR system because its effectiveness depends heavily on the set of features used to realise the system.A distinguishing feature of this volume is that it deals with all three aspects of PR, namely feature analysis, clustering and classifier design. It also encompasses image processing methodologies and image retrieval with subjective information. The other interesting aspect of the volume is that it covers all three major facets of soft computing: fuzzy logic, neural networks and evolutionary computing.

Pattern Recognition Algorithms for Data Mining addresses different pattern recognition (PR) tasks in a unified framework with both theoretical and experimental results. Tasks covered include data condensation, feature selection, case generation, clustering/classification, and rule generation and evaluation. This volume presents various theories, methodologies, and algorithms, using both classical approaches and hybrid paradigms. The authors emphasize large datasets with overlapping, intractable, or nonlinear boundary classes, and datasets that demonstrate granular computing in soft frameworks. Organized into eight chapters, the book begins with an introduction to PR, data mining, and knowledge discovery concepts. The authors analyze the tasks of multi-scale data condensation and dimensionality reduction, then explore the problem of learning with support vector machine (SVM). They conclude by highlighting the significance of granular computing for different mining tasks in a soft paradigm.

This book presents practical development experiences in different areas of data analysis and pattern recognition, focusing on soft computing technologies, clustering and classification algorithms, rough set and fuzzy set theory, evolutionary computations, neural science and neural network systems, image processing, combinatorial pattern matching, social network analysis, audio and video data analysis, data mining in dynamic environments, bioinformatics, hybrid computing, big data analytics and deep learning. It also provides innovative solutions to the challenges in these areas and discusses recent developments.

Pattern recognizers evolve across the sections into perceptrons, a layer of perceptrons, multiple-layered perceptrons, functional link nets, and radial basis function networks. Other networks covered in the process are learning vector quantization networks, self-organizing maps, and recursive neural networks. Backpropagation is derived in complete detail for one and two hidden layers for both unipolar and bipolar sigmoid activation functions.