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Neural Networks for Knowledge Representation and Inference

presented here not only address the theory and implementation of artificial neural networks for higher cognitive functions, but also critique the;

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Neural Networks And Artificial Intelligence For Biomedical Engineering

biomedicine based on current theory and applications of neural networks, artificial intelligence, and other methods for the development of decision;

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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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Soft Starting of Induction Motors

in torque were reduced significantly.Radial basis and feed forward neural networks werecompared for off-line and on-line training, trainingtime;

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Bayesian Networks for Reliability Engineering

) is considered to be one of the most powerful models in probabilistic knowledge representation and inference, and it is increasingly used in the field;

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Artificial Intelligence

and inference, non-monotonic logic, reason maintenance, memory organization, probabilistic reasoning, induction and neural networks. The;

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Bio-Intelligence Science

for speech recognition based on neural networks, and deep recurrent neural network-based auto-encoders for acoustic novelty detection.;

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Graph Representation Learning

. Building relational inductive biases into deep learning architectures is crucial for creating systems that can learn, reason, and generalize from;

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Network Models for Control and Processing

of biological experimentation, and representation of artificial neural networks (ANNs) Within the fields mentioned, the topics discussed include;

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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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Complex-valued Neural Networks

Recent research indicates that complex-valued neural networks whose parameters (weights and threshold values) are all complex numbers are;

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Deep Neuro-Fuzzy Systems with Python

Gain insight into fuzzy logic and neural networks, and how the integration between the two models makes intelligent systems in the current;

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Discovery And Fusion Of Uncertain Knowledge In Data

Data analysis is of upmost importance in the mining of big data, where knowledge discovery and inference are the basis for intelligent;

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Neural Networks with R

intelligence and deep learning and you want to level up, then this book is what you need! What You Will Learn * Set up R packages for neural;

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Trends in Deep Learning Methodologies

learn the patterns and representation of the given data for the development of specific models.;

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Applications of Fuzzy Sets and the Theory of Evidence to Accounting

; and neural networks.;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Artificial Neural Networks and Machine Learning - ICANN 2017

; Convolutional Neural Networks; Games and Strategy; Representation and Classification; Clustering; Learning from Data Streams and Time Series; Image;

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Handbook on Neural Information Processing

This handbook presents some of the most recent topics in neural information processing, covering both theoretical concepts and practical;

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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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Neural Information Processing

; convolutional neural networks; deep neural networks; feature learning and representation; human centred computing; human centred computing and medicine;

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Artificial Intelligence

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice;

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Artificial Intelligence

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice;

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Artificial Intelligence

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice;

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Artificial Intelligence

The long-anticipated revision of this best-selling book offers the most comprehensive, up-to-date introduction to the theory and practice;

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Nonlinear Dynamical Systems

, this volume is an indispensable resource for researchers in neural networks and a reference staple for libraries.;

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