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This book presents an overview of the exciting, truly multidisciplinary research by neuroscientists and systems engineers in the emerging field of cognitive systems, providing a cross-disciplinary examination of this cutting-edge area of scientific research. This is a great example of where research in very different disciplines touches to create a new emerging area of research. The book illustrates some of the technical developments that could arise from our growing understanding of how living cognitive systems behave, and the ability to use that knowledge in the design of artificial systems.
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A collection of selected papers from the 18th WIRN workshop, the annual meeting of the Italian Neural Networks Society (SIREN). It is divided in two general subjects, 'models' and 'applications' and two specific ones, 'economy and complexity' and 'remote sensing image processing'.
Neural networks (Computer science) --- Computers. --- Automatic computers --- Automatic data processors --- Computer hardware --- Computing machines (Computers) --- Electronic brains --- Electronic calculating-machines --- Electronic computers --- Hardware, Computer --- Computer systems --- Cybernetics --- Machine theory --- Calculators --- Cyberspace --- E-books --- Réseaux neuronaux (informatique)
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The proceedings of the 2001 Neural Information Processing Systems (NIPS) Conference.The annual conference on Neural Information Processing Systems (NIPS) is the flagship conference on neural computation. The conference is interdisciplinary, with contributions in algorithms, learning theory, cognitive science, neuroscience, vision, speech and signal processing, reinforcement learning and control, implementations, and diverse applications. Only about 30 percent of the papers submitted are accepted for presentation at NIPS, so the quality is exceptionally high. These proceedings contain all of the papers that were presented at the 2001 conference.
Neural networks (Computer science) --- Neural computers --- Réseaux neuronaux (Informatique) --- Ordinateurs neuronaux --- Congresses --- Congrès --- Human Anatomy & Physiology --- Neuroscience --- Health & Biological Sciences --- Réseaux neuronaux (Informatique) --- Congrès --- Neural circuitry --- Information storage and retrieval systems --- E-books --- COMPUTER SCIENCE/Machine Learning & Neural Networks
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Image Processing and Pattern Recognition covers major applications in the field, including optical character recognition, speech classification, medical imaging, paper currency recognition, classification reliability techniques, and sensor technology. The text emphasizes algorithms and architectures for achieving practical and effective systems, and presents many examples. Practitioners, researchers, and students in computer science, electrical engineering, andradiology, as well as those working at financial institutions, will value this unique and authoritative reference to diverse app
Image processing --- Pattern recognition systems. --- Pattern classification systems --- Pattern recognition computers --- Pattern perception --- Computer vision --- Digital image processing --- Digital electronics --- Digital techniques. --- Neural networks (Computer science) --- Réseaux neuronaux (Informatique) --- Pattern recognition systems --- Traitement d'images --- Reconnaissance des formes (Informatique) --- Digital techniques --- Techniques numériques
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Learning --- Neural networks (Computer science) --- Neural computers --- Neuroscience --- Human Anatomy & Physiology --- Health & Biological Sciences --- Physiological aspects --- Computer simulation --- Neural net computers --- Neural network computers --- Neurocomputers --- Learning process --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Neural networks. --- Comprehension --- Education --- Neuropsychology --- Physiological aspects. --- Computer simulation. --- Neural computers. --- Electronic digital computers --- Natural computation --- Artificial intelligence --- Cognitive psychology --- Artificial intelligence. Robotics. Simulation. Graphics --- Neural networks (Computer science). --- Apprentissage --- Réseaux neuronaux (Informatique) --- Ordinateurs neuronaux --- Aspect physiologique
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Neural networks (Neurobiology) --- Neural networks (Computer science) --- Artificial intelligence --- Brain --- Réseaux neuronaux (Neurobiologie) --- Réseaux neuronaux (Informatique) --- Intelligence artificielle --- Cerveau --- Handbooks, manuals, etc --- -#TELE:SISTA --- #KVHB:Neurologie --- #KVHB:Hersenen --- Biological neural networks --- Nets, Neural (Neurobiology) --- Networks, Neural (Neurobiology) --- Neural nets (Neurobiology) --- Cognitive neuroscience --- Neurobiology --- Neural circuitry --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Natural computation --- Soft computing --- Handbooks, manuals, etc. --- Neurale netwerken. --- Connectionisme. --- Neural networks (Neurobiology). --- Neural networks (Computer science). --- Réseaux neuronaux (Neurobiologie) --- Réseaux neuronaux (Informatique) --- Computer. Automation --- Artificial intelligence. --- #TELE:SISTA --- Neural networks (Neurobiology) - Handbooks, manuals, etc --- Neural networks (Computer science) - Handbooks, manuals, etc
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Neural computers --- Neural networks (Computer science) --- Neural networks (Neurobiology) --- Nerve Net --- Nervous System --- Ordinateurs neuronaux --- Réseaux neuronaux (Informatique) --- Réseaux neuronaux (Neurobiologie) --- Périodiques. --- Neural net computers --- Neural network computers --- Neurocomputers --- Nervous Systems --- System, Nervous --- Systems, Nervous --- Biological neural networks --- Nets, Neural (Neurobiology) --- Networks, Neural (Neurobiology) --- Neural nets (Neurobiology) --- Nerve Net. --- Nervous System. --- Neural Networks (Anatomic) --- Nerve Nets --- Net, Nerve --- Nets, Nerve --- Network, Neural (Anatomic) --- Networks, Neural (Anatomic) --- Neural Network (Anatomic) --- Electronic digital computers --- Natural computation --- Artificial intelligence --- Cognitive neuroscience --- Neurobiology --- Neural circuitry
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Probabilistic graphical models and decision graphs are powerful modeling tools for reasoning and decision making under uncertainty. As modeling languages they allow a natural specification of problem domains with inherent uncertainty, and from a computational perspective they support efficient algorithms for automatic construction and query answering. This includes belief updating, finding the most probable explanation for the observed evidence, detecting conflicts in the evidence entered into the network, determining optimal strategies, analyzing for relevance, and performing sensitivity analysis. The book introduces probabilistic graphical models and decision graphs, including Bayesian networks and influence diagrams. The reader is introduced to the two types of frameworks through examples and exercises, which also instruct the reader on how to build these models. The book is a new edition of Bayesian Networks and Decision Graphs by Finn V. Jensen. The new edition is structured into two parts. The first part focuses on probabilistic graphical models. Compared with the previous book, the new edition also includes a thorough description of recent extensions to the Bayesian network modeling language, advances in exact and approximate belief updating algorithms, and methods for learning both the structure and the parameters of a Bayesian network. The second part deals with decision graphs, and in addition to the frameworks described in the previous edition, it also introduces Markov decision processes and partially ordered decision problems. The authors also provide a well-founded practical introduction to Bayesian networks, object-oriented Bayesian networks, decision trees, influence diagrams (and variants hereof), and Markov decision processes. give practical advice on the construction of Bayesian networks, decision trees, and influence diagrams from domain knowledge. < give several examples and exercises exploiting computer systems for dealing with Bayesian networks and decision graphs. present a thorough introduction to state-of-the-art solution and analysis algorithms. The book is intended as a textbook, but it can also be used for self-study and as a reference book. Finn V. Jensen is a professor at the department of computer science at Aalborg University, Denmark. Thomas D. Nielsen is an associate professor at the same department.
Mathematical statistics --- Computer Science. --- Probability and Statistics in Computer Science. --- Artificial Intelligence (incl. Robotics). --- Statistics for Engineering, Physics, Computer Science, Chemistry & Geosciences. --- Computer science. --- Artificial intelligence. --- Statistics. --- Informatique --- Intelligence artificielle --- Statistique --- Bayesian statistical decision theory --- Machine Learning --- Neural networks (Computer science) --- Decision Making --- Data processing --- Bayesian statistical decision theory -- Data processing. --- Decision making. --- Electronic books. -- local. --- Machine learning. --- Neural networks (Computer science). --- Mathematics --- Physical Sciences & Mathematics --- Mathematical Statistics --- Machine learning --- Decision making --- Data processing. --- Artificial neural networks --- Nets, Neural (Computer science) --- Networks, Neural (Computer science) --- Neural nets (Computer science) --- Learning, Machine --- Deciding --- Decision (Psychology) --- Decision analysis --- Decision processes --- Making decisions --- Management --- Management decisions --- Bayes' solution --- Bayesian analysis --- Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- 681.3*I28 Problem solving, control methods and search: backtracking; dynamic program- ming; graph and tree search strategies; heuristics; plan execution, formationand generation (Artificial intelligence)--See also {681.3*F22} --- Statistique bayésienne --- Life sciences. --- Mathematical statistics. --- Plant science. --- Botany. --- Probabilities. --- Life Sciences. --- Plant Sciences. --- Probability Theory and Stochastic Processes. --- Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences. --- 519.2 --- 681.3*I28 --- Artificial intelligence --- Natural computation --- Soft computing --- Machine theory --- Choice (Psychology) --- Problem solving --- Statistical decision --- 519.2 Probability. Mathematical statistics --- Probability. Mathematical statistics --- Statistical analysis --- Statistical data --- Statistical methods --- Statistical science --- Econometrics --- Probability --- Statistical inference --- Combinations --- Chance --- Least squares --- Risk --- Botanical science --- Phytobiology --- Phytography --- Phytology --- Plant biology --- Plant science --- Biology --- Natural history --- Plants --- 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 --- Statistics, Mathematical --- Statistics --- Probabilities --- Sampling (Statistics) --- Biosciences --- Sciences, Life --- Science --- Apprentissage automatique --- Réseaux neuronaux (Informatique) --- Prise de décision --- Distribution (Probability theory. --- Artificial Intelligence. --- Informatics --- Distribution functions --- Frequency distribution --- Characteristic functions --- Prise de décision. --- Apprentissage automatique. --- Informatique. --- Statistics . --- Floristic botany --- Bayesian statistical decision theory - Data processing
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