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A Geometric Approach to the Unification of Symbolic Structures and Neural Networks

The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged;

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A Geometric Approach to the Unification of Symbolic Structures and Neural Networks

The unification of symbolist and connectionist models is a major trend in AI. The key is to keep the symbolic semantics unchanged;

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Knowledge-Based Neurocomputing

integrates the merits of symbolic fuzzy rule-bases and sub-symbolic artificial neural networks, and yields a new approach for knowledge-based;

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Computational Architectures Integrating Neural and Symbolic Processes

aspects of the integration of neural and symbolic processes. In order to provide a basis for a deeper understanding of existing divergent;

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Machine Learning Control by Symbolic Regression

learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction;

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Neural Networks for Knowledge Representation and Inference

The second published collection based on a conference sponsored by the Metroplex Institute for Neural Dynamics -- the first is Motivation;

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Spatially Explicit Hyperparameter Optimization for Neural Networks

networks (CNNs), have been extensively used in the GIScience domain to explore the nonlinear and complex geographic phenomena. However, there are a;

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Nonlinear Dynamical Systems

The first truly up-to-date look at the theory and capabilities of nonlinear dynamical systems that take the form of feedforward neural;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Neural Network Methods in Natural Language Processing

Neural networks are a family of powerful machine learning models and this book focuses on their application to natural language data;

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Deep Neural Networks in a Mathematical Framework

to represent and describe neural networks, casting previous results in the field in a more natural light. In particular, the authors derive;

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Advanced Models of Neural Networks

This book provides a complete study on neural structures exhibiting nonlinear and stochastic dynamics, elaborating on neural dynamics by;

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Introduction to Deep Learning and Neural Networks with PythonT

>Examines the practical side of deep learning and neural networks Provides a problem-based approach to building artificial neural networks;

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Exploring Cognition: Damaged Brains and Neural Networks

and innovative approach leads to re-evaluation of traditional theories: connectionist models lesioned to mimic the residual function of the;

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Deep Neural Networks and Applications

to main neural networks (MNN). The book throws light on the prospect of artificial intelligence and the applications it has in risk;

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Subsymbolic Natural Language Processing

. Using the DISCERN system as an example, he describes a general approach to building high-level cognitive models from distributed neural networks;

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Exploring Neural Networks with C#

is easy to understand. Taking a learn by doing approach, the book is filled with illustrations to guide you through the mystery of neural;

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Deep Learning from Scratch Building with Python from First Principles

along the way. Author Seth Weidman shows you how neural networks work using a first principles approach. You'll learn how to apply multilayer;

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Exploring Neural Networks with C#

is easy to understand.Taking a learn by doing approach, the book is filled with illustrations to guide you through the mystery of neural;

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Neural Networks with R

will learn to implement neural network models in your applications with the help of practical examples in the book. Style and approach A;

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Credit Rating Modelling by Neural Networks

includes data pre-processing, the selection process of input variables, and the design of various neural networks' structures for classification.;

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New Developments in Computer Networks

of prediction performance based on real network traffic; network security; a modified artificial neural network based approach to add new data in a;

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Hierarchical Modular Granular Neural Networks with Fuzzy Aggregation

applied in two levels. The techniques used and combined in the proposed method are modular neural networks (MNNs) with a Granular Computing (GrC;

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Cellular Neural Networks

Cellular Neural Networks (CNN's) were introduced in 1988 by L O Chua and L Yang as a novel class of information processing systems, which;

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Advanced Neural Network-Based Computational Schemes for Robust Fault Diagnosis

The present book is devoted to problems of adaptation of artificial neural networks to robust fault diagnosis schemes. It presents neural;

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Systems, Patterns and Data Engineering with Geometric Calculi

essentials of geometric calculus (GC) formalism, to report on state-of-the-art applications showcasing its advantages and to explore the bearing;

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Pattern Recognition with Neural Networks in C++

The addition of artificial neural network computing to traditional pattern recognition has given rise to a new, different, and more;

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