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This book is the outcome of a decade’s research into a speci?c architecture and associated learning mechanism for an arti?cial neural network: the - chitecture involves negative feedback and the learning mechanism is simple Hebbian learning. The research began with my own thesis at the University of Strathclyde, Scotland, under Professor Douglas McGregor which culminated with me being awarded a PhD in 1995 [52], the title of which was “Negative Feedback as an Organising Principle for Arti?cial Neural Networks”. Naturally enough, having established this theme, when I began to sup- vise PhD students of my own, we continued to develop this concept and this book owes much to the research and theses of these students at the Applied Computational Intelligence Research Unit in the University of Paisley. Thus we discuss work from • Dr. Darryl Charles [24] in Chapter 5. • Dr. Stephen McGlinchey [127] in Chapter 7. • Dr. Donald MacDonald [121] in Chapters 6 and 8. • Dr. Emilio Corchado [29] in Chapter 8. We brie?y discuss one simulation from the thesis of Dr. Mark Girolami [58] in Chapter 6 but do not discuss any of the rest of his thesis since it has already appeared in book form [59]. We also must credit Cesar Garcia Osorio, a current PhD student, for the comparative study of the two Exploratory Projection Pursuit networks in Chapter 8. All of Chapters 3 to 8 deal with single stream arti?cial neural networks.

Computer science. --- Mathematical statistics. --- Artificial intelligence. --- Computer simulation. --- Pattern recognition. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Probability and Statistics in Computer Science. --- Pattern Recognition. --- Simulation and Modeling. --- Computer Science, general. --- Design perception --- Pattern recognition --- Form perception --- Perception --- Figure-ground perception --- Computer modeling --- Computer models --- Modeling, Computer --- Models, Computer --- Simulation, Computer --- Electromechanical analogies --- Mathematical models --- Simulation methods --- Model-integrated computing --- 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 --- Fifth generation computers --- Neural computers --- Mathematics --- Statistical inference --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Informatics --- Science --- Statistical methods --- Neural networks (Computer science) --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Artificial intelligence --- Natural computation --- Soft computing --- Optical pattern recognition. --- Artificial Intelligence. --- Optical data processing --- Pattern perception --- Perceptrons --- Visual discrimination

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Theoretical spectroscopy. Spectroscopic techniques --- fysicochemie

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This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning, IDEAL 2008, held in Daejeon, Korea, in November 2008. The 56 revised full papers presented together with 10 invited papers were carefully reviewed and selected from numerous submissions for inclusion in the book. The papers are organized in topical sections on learning and information processing, data mining and information management, bioinformatics and neuroinformatics, agents and distributed systems, as well as financial engineering and modeling.

Database management --- Data mining --- Intelligent agents (Computer software) --- Database management. --- Computer software. --- Artificial intelligence. --- Data mining. --- Bioinformatics. --- Database Management. --- Algorithm Analysis and Problem Complexity. --- Artificial Intelligence. --- Information Systems Applications (incl. Internet). --- Data Mining and Knowledge Discovery. --- Computational Biology/Bioinformatics. --- Bio-informatics --- Biological informatics --- Biology --- Information science --- Computational biology --- Systems biology --- Algorithmic knowledge discovery --- Factual data analysis --- KDD (Information retrieval) --- Knowledge discovery in data --- Knowledge discovery in databases --- Mining, Data --- Database searching --- 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 --- Software, Computer --- Computer systems --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Data processing --- Algorithms. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Algorism --- Algebra --- Arithmetic --- Foundations --- Computer science --- Algorithms --- Research. --- Study and teaching. --- Informatics --- Science

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The IDEAL conference has become a unique, established and broad interdisciplinary forum for experts, researchers and practitioners in many fields to interact with each other and with leading academics and industries in the areas of machine learning, information processing, data mining, knowledge management, bio-informatics, neu- informatics, bio-inspired models, agents and distributed systems, and hybrid systems. This volume contains the papers presented at the 11th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL 2010), which was held September 1–3, 2010 in the University of the West of Scotland, on its Paisley campus, 15 kilometres from the city of Glasgow, Scotland. All submissions were strictly pe- reviewed by the Programme Committee and only the papers judged with sufficient quality and novelty were accepted and included in the proceedings. The IDEAL conferences continue to evolve and this year’s conference was no exc- tion. The conference papers cover a wide variety of topics which can be classified by technique, aim or application. The techniques include evolutionary algorithms, artificial neural networks, association rules, probabilistic modelling, agent modelling, particle swarm optimization and kernel methods. The aims include regression, classification, clustering and generic data mining. The applications include biological information processing, text processing, physical systems control, video analysis and time series analysis.

Database management --- Data mining --- Intelligent agents (Computer software) --- Engineering & Applied Sciences --- Mechanical Engineering --- Computer Science --- Mechanical Engineering - General --- Information Technology --- Artificial Intelligence --- Computer science. --- Computer programming. --- Computers. --- Database management. --- Information storage and retrieval. --- Artificial intelligence. --- Computer Science. --- Artificial Intelligence (incl. Robotics). --- Programming Techniques. --- Database Management. --- Information Systems Applications (incl. Internet). --- Information Storage and Retrieval. --- Computation by Abstract Devices. --- 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 --- Data base management --- Data services (Database management) --- Database management services --- DBMS (Computer science) --- Generalized data management systems --- Services, Database management --- Systems, Database management --- Systems, Generalized database management --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Calculators --- Cyberspace --- Computers --- Electronic computer programming --- Electronic digital computers --- Programming (Electronic computers) --- Coding theory --- Informatics --- Science --- Programming --- Information storage and retrieva. --- Artificial Intelligence. --- Information storage and retrieval systems. --- Application software. --- Application computer programs --- Application computer software --- Applications software --- Apps (Computer software) --- Computer software --- Pasley <2010>

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Exploratory data analysis, also known as data mining or knowledge discovery from databases, is typically based on the optimisation of a specific function of a dataset. Such optimisation is often performed with gradient descent or variations thereof. In this book, we first lay the groundwork by reviewing some standard clustering algorithms and projection algorithms before presenting various non-standard criteria for clustering. The family of algorithms developed are shown to perform better than the standard clustering algorithms on a variety of datasets. We then consider extensions of the basic mappings which maintain some topology of the original data space. Finally we show how reinforcement learning can be used as a clustering mechanism before turning to projection methods. We show that several varieties of reinforcement learning may also be used to define optimal projections for example for principal component analysis, exploratory projection pursuit and canonical correlation analysis. The new method of cross entropy adaptation is then introduced and used as a means of optimising projections. Finally an artificial immune system is used to create optimal projections and combinations of these three methods are shown to outperform the individual methods of optimisation.

Engineering sciences. Technology --- Artificial intelligence. Robotics. Simulation. Graphics --- analyse (wiskunde) --- gegevensanalyse --- ingenieurswetenschappen --- robots