2006, ISBN: 9783540464174
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Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This bo… Mehr…
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2006, ISBN: 9783540464174
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2006, ISBN: 9783540464174
eBooks, eBook Download (PDF), Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction wit… Mehr…
ISBN: 9783540464174
Hybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This bo… Mehr…
ISBN: 9783540464174
Hybrid Neural Systems ab 149.99 € als pdf eBook: . Aus dem Bereich: eBooks, Fachthemen & Wissenschaft, Medizin, Medien > Bücher, Hybrid Neural Systems - eBook als pdf von - Springer Berl… Mehr…
2006, ISBN: 9783540464174
eBook Download (PDF), eBooks, [PU: Springer Berlin Heidelberg]
ISBN: 9783540464174
Hybrid Neural Systems: ab 149.99 € Medien > Bücher > E-books, [PU: Springer, Berlin/Heidelberg]
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Detailangaben zum Buch - Hybrid Neural Systems
EAN (ISBN-13): 9783540464174
Erscheinungsjahr: 2006
Herausgeber: Springer Berlin Heidelberg
Buch in der Datenbank seit 2017-02-25T20:11:28+01:00 (Berlin)
Detailseite zuletzt geändert am 2022-02-09T02:45:05+01:00 (Berlin)
ISBN/EAN: 9783540464174
ISBN - alternative Schreibweisen:
978-3-540-46417-4
Alternative Schreibweisen und verwandte Suchbegriffe:
Autor des Buches: wermter
Daten vom Verlag:
Autor/in: Stefan Wermter; Ron Sun
Titel: Lecture Notes in Computer Science; Lecture Notes in Artificial Intelligence; Hybrid Neural Systems
Verlag: Springer; Springer Berlin
408 Seiten
Erscheinungsjahr: 2006-12-30
Berlin; Heidelberg; DE
Sprache: Englisch
96,29 € (DE)
99,00 € (AT)
118,00 CHF (CH)
Available
IX, 408 p.
EA; E107; eBook; Nonbooks, PBS / Medizin/Nichtklinische Fächer; Neurowissenschaften; Verstehen; Fuzzy; Neural systems; algorithmic learning; artificial neural networks; classification; cognitive science; connectionism; hybrid neural systems; learning; neural architectures; neuroscience; robot; robotics; C; Neuroscience; Artificial Intelligence; Theory of Computation; Processor Architectures; Computer Science; Künstliche Intelligenz; Theoretische Informatik; Rechnerarchitektur und Logik-Entwurf; BC
An Overview of Hybrid Neural Systems.- An Overview of Hybrid Neural Systems.- Structured Connectionism and Rule Representation.- Layered Hybrid Connectionist Models for Cognitive Science.- Types and Quantifiers in SHRUTI – A Connectionist Model of Rapid Reasoning and Relational Processing.- A Recursive Neural Network for Reflexive Reasoning.- A Novel Modular Neural Architecture for Rule-Based and Similarity-Based Reasoning.- Addressing Knowledge-Representation Issues in Connectionist Symbolic Rule Encoding for General Inference.- Towards a Hybrid Model of First-Order Theory Refinement.- Distributed Neural Architectures and Language Processing.- Dynamical Recurrent Networks for Sequential Data Processing.- Fuzzy Knowledge and Recurrent Neural Networks: A Dynamical Systems Perspective.- Combining Maps and Distributed Representations for Shift-Reduce Parsing.- Towards Hybrid Neural Learning Internet Agents.- A Connectionist Simulation of the Empirical Acquisition of Grammatical Relations.- Large Patterns Make Great Symbols: An Example of Learning from Example.- Context Vectors: A Step Toward a “Grand Unified Representation”.- Integration of Graphical Rules with Adaptive Learning of Structured Information.- Transformation and Explanation.- Lessons from Past, Current Issues, and Future Research Directions in Extracting the Knowledge Embedded in Artificial Neural Networks.- Symbolic Rule Extraction from the DIMLP Neural Network.- Understanding State Space Organization in Recurrent Neural Networks with Iterative Function Systems Dynamics.- Direct Explanations and Knowledge Extraction from a Multilayer Perceptron Network that Performs Low Back Pain Classification.- High Order Eigentensors as Symbolic Rules in Competitive Learning.- Holistic Symbol Processing and theSequential RAAM: An Evaluation.- Robotics, Vision and Cognitive Approaches.- Life, Mind, and Robots.- Supplementing Neural Reinforcement Learning with Symbolic Methods.- Self-Organizing Maps in Symbol Processing.- Evolution of Symbolisation: Signposts to a Bridge between Connectionist and Symbolic Systems.- A Cellular Neural Associative Array for Symbolic Vision.- Application of Neurosymbolic Integration for Environment Modelling in Mobile Robots.Includes supplementary material: sn.pub/extras
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