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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

Pattern perception. --- Machine learning. --- Genetic algorithms. --- Automatic classification. --- GAs (Algorithms) --- Genetic searches (Algorithms) --- Algorithms --- Combinatorial optimization --- Evolutionary computation --- Genetic programming (Computer science) --- Learning classifier systems --- Learning, Machine --- Artificial intelligence --- Machine theory --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Automatic indexing --- Classification --- Information storage and retrieval systems --- Optical pattern recognition. --- Computer science. --- Telecommunication. --- Artificial intelligence. --- Bioinformatics. --- Pattern Recognition. --- Programming Techniques. --- Communications Engineering, Networks. --- Artificial Intelligence. --- Complex Systems. --- Computational Biology/Bioinformatics. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Electric communication --- Mass communication --- Telecom --- Telecommunication industry --- Telecommunications --- Communication --- Information theory --- Telecommuting --- Informatics --- Science --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination --- Data processing --- Pattern recognition. --- Computer programming. --- Electrical engineering. --- Statistical physics. --- Dynamical systems. --- Dynamical systems --- Kinetics --- Mathematics --- Mechanics, Analytic --- Force and energy --- Mechanics --- Physics --- Statics --- Mathematical statistics --- Electric engineering --- Engineering --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Statistical methods --- Programming --- Pattern recognition systems. --- System theory. --- Automated Pattern Recognition. --- Computational and Systems Biology. --- Systems, Theory of --- Systems science --- Pattern classification systems --- Pattern recognition computers --- Computer vision --- Philosophy

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This book provides a unified framework that describes how genetic learning can be used to design pattern recognition and learning systems. The book is unique in the sense of describing how a search technique, the genetic algorithm, can be used for pattern classification mainly through approximating decision boundaries, and it demonstrates the effectiveness of the genetic classifiers vis-à-vis several widely used classifiers, including neural networks. It provides a balanced mixture of theories, algorithms and applications, and in particular results from the bioinformatics and Web intelligence domains. This book will be useful to graduate students and researchers in computer science, electrical engineering, systems science, and information technology, both as a text and reference book. Researchers and practitioners in industry working in system design, control, pattern recognition, data mining, soft computing, bioinformatics and Web intelligence will also benefit.

Mathematical statistics --- Molecular biology --- Computer science --- Programming --- Information systems --- Artificial intelligence. Robotics. Simulation. Graphics --- patroonherkenning --- factoranalyse --- toegepaste informatica --- bio-informatica --- database management --- KI (kunstmatige intelligentie) --- robots --- moleculaire biologie

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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 82 revised papers presented were carefully reviewed and selected from 241 submissions. 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.

Pattern perception --- Pattern recognition systems --- Artificial intelligence --- Perception des structures --- Reconnaissance des formes (Informatique) --- Intelligence artificielle --- Congresses. --- Congrès --- Computer Science --- Engineering & Applied Sciences --- Computer science. --- Computer communication systems. --- Computers. --- Data mining. --- Artificial intelligence. --- Pattern recognition. --- Computer Science. --- Pattern Recognition. --- Computation by Abstract Devices. --- Artificial Intelligence (incl. Robotics). --- Information Systems Applications (incl. Internet). --- Data Mining and Knowledge Discovery. --- Computer Communication Networks. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- AI (Artificial intelligence) --- Artificial thinking --- Electronic brains --- Intellectronics --- Intelligence, Artificial --- Intelligent machines --- Machine intelligence --- Thinking, Artificial --- Bionics --- Cognitive science --- Digital computer simulation --- Electronic data processing --- Logic machines --- Machine theory --- Self-organizing systems --- Simulation methods --- Fifth generation computers --- Neural computers --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Communication systems, Computer --- Computer communication systems --- Data networks, Computer --- ECNs (Electronic communication networks) --- Electronic communication networks --- Networks, Computer --- Teleprocessing networks --- Data transmission systems --- Digital communications --- Electronic systems --- Information networks --- Telecommunication --- Cyberinfrastructure --- Network computers --- Informatics --- Science --- Distributed processing --- Optical pattern recognition. --- Artificial Intelligence. --- Optical data processing --- Perceptrons --- Visual discrimination --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Pattern recognition systems. --- Computer networks. --- Automated Pattern Recognition. --- Theory of Computation. --- Computer and Information Systems Applications. --- Pattern classification systems --- Pattern recognition computers --- Computer vision