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020 _a9783540464174
_9978-3-540-46417-4
024 7 _a10.1007/10719871
_2doi
050 4 _aRC321-580
072 7 _aPSAN
_2bicssc
072 7 _aSCI089000
_2bisacsh
072 7 _aPSAN
_2thema
082 0 4 _a612.8
_223
245 1 0 _aHybrid Neural Systems
_h[electronic resource] /
_cedited by Stefan Wermter, Ron Sun.
250 _a1st ed. 2000.
264 1 _aBerlin, Heidelberg :
_bSpringer Berlin Heidelberg :
_bImprint: Springer,
_c2000.
300 _aIX, 408 p.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1778
505 0 _aAn 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.
520 _aHybrid neural systems are computational systems which are based mainly on artificial neural networks and allow for symbolic interpretation or interaction with symbolic components. This book is derived from a workshop held during the NIPS'98 in Denver, Colorado, USA, and competently reflects the state of the art of research and development in hybrid neural systems. The 26 revised full papers presented together with an introductory overview by the volume editors have been through a twofold process of careful reviewing and revision. The papers are organized in the following topical sections: structured connectionism and rule representation; distributed neural architectures and language processing; transformation and explanation; robotics, vision, and cognitive approaches.
650 0 _aNeurosciences.
650 0 _aArtificial intelligence.
650 0 _aComputer science.
650 0 _aMicroprocessors.
650 0 _aComputer architecture.
650 1 4 _aNeuroscience.
650 2 4 _aArtificial Intelligence.
650 2 4 _aTheory of Computation.
650 2 4 _aProcessor Architectures.
700 1 _aWermter, Stefan.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
700 1 _aSun, Ron.
_eeditor.
_4edt
_4http://id.loc.gov/vocabulary/relators/edt
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783540673057
776 0 8 _iPrinted edition:
_z9783662180006
830 0 _aLecture Notes in Artificial Intelligence,
_x2945-9141 ;
_v1778
856 4 0 _uhttps://doi.org/10.1007/10719871
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