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Statistical Mechanics of Neural Networks

This book highlights a comprehensive introduction to the fundamental statistical mechanics underneath the inner workings of neural networks;

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Information-Theoretic Aspects of Neural Networks

neural network training non-conventional, statistical distance-measures for neural network optimizations symmetric and asymmetric;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Statistical Learning Using Neural Networks

Statistical Learning using Neural Networks: A Guide for Statisticians and Data Scientists with Python introduces artificial neural networks;

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Mathematical Perspectives on Neural Networks

-- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.;

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Mathematical Perspectives on Neural Networks

-- computational, dynamical, and statistical; the other assembles these three perspectives into a unified overview of the neural networks field.;

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Quantum Computing

are integrated from statistical physics, information theory, and computer science. Statistical neural field theory and the AdS/CFT;

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Principles Of Artificial Neural Networks

The field of Artificial Neural Networks is the fastest growing field in Information Technology and specifically, in Artificial Intelligence;

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Neural Networks and Statistical Learning

, comprehensive resource for study and further research, it explores the major popular neural network models and statistical learning approaches with;

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Statistical Field Theory for Neural Networks

This book presents a self-contained introduction to techniques from field theory applied to stochastic and collective dynamics in neuronal;

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Statistical Signal Processing for Neuroscience and Neurotechnology

This is a uniquely comprehensive reference that summarizes the state of the art of signal processing theory and techniques for solving;

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Stochastic Models of Neural Networks

This book is intended to provide a treatment of the theory and applications of Stochastic Neural Networks,that is networks able to learn;

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Kinematic Control of Redundant Robot Arms Using Neural Networks

presents and investigates different methods and schemes for the control of robotic arms whilst exploring the field from all angles. On a more;

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Brain Theory and Neural Networks

it will provide in-depth knowledge about the theory and practice of brain theory and neural networks. With state-of-the-art inputs by;

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Spatially Explicit Hyperparameter Optimization for Neural Networks

depends on the parameter setting for a given dataset. Meanwhile, adjusting the parameter configuration of neural networks will increase the;

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Handbook on Neural Information Processing

Approximation capabilities of neural networks Semi-supervised learning Statistical relational learning Kernel methods for structured data Multiple;

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Artificial Neural Networks in Biological and Environmental Analysis

research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers;

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Artificial Neural Networks in Biological and Environmental Analysis

research and teaching programs, but also for the continued interest in the use of neural network tools in scientific inquiry. The book covers;

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Advanced Models of Neural Networks

examines their stability properties with the use of dynamical systems theory. It is suitable for researchers and postgraduate students engaged;

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Neural Networks for Knowledge Representation and Inference

neural network theory. A particular issue is how well neural networks -- well established for statistical pattern matching -- can perform the;

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Cognitive Computing: Theory and Applications

, machine learning models and algorithms, biometrics, Kernel Based Models for transductive learning, neural networks, graph analytics in cyber;

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Blind Equalization in Neural Networks

The book begins with an introduction of blind equalization theory and its application in neural networks, then discusses the algorithms;

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Pattern Classification

in pattern classification, this book offers a theory-based engineering perspective on neural networks and statistical pattern classification. Pattern;

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Statistical Physics of Spin Glasses and Information Processing

a general knowledge of statistical mechanics and basic probability theory. It provides the reader with a sound introduction to the field;

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From Thermal Field Theory To Neural Networks

1994, he was only 31.A meeting was organized at CERN, covering the various aspects of his scientific interests: thermal field theory and its;

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Artificial Neural Networks - ICANN 2009

and covers the field of neurocomputing, learning systems and related areas. Artificial neural networks provide an information-processing;

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Stevens' Handbook of Experimental Psychology

; network models for clinical psychology; response time modeling; analyzing neural time series data; models and methods for reinforcement learning;

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