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020 _a9783031573125
_9978-3-031-57312-5
024 7 _a10.1007/978-3-031-57312-5
_2doi
050 4 _aTA345-345.5
072 7 _aUN
_2bicssc
072 7 _aCOM018000
_2bisacsh
072 7 _aUN
_2thema
100 1 _aHoya, Tetsuya.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
_977141
245 1 0 _aSyntactic Networks—Kernel Memory Approach
_h[electronic resource] /
_cby Tetsuya Hoya.
250 _a1st ed. 2024.
264 1 _aCham :
_bSpringer Nature Switzerland :
_bImprint: Springer,
_c2024.
300 _aXV, 129 p. 31 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1157
505 0 _aReview of the Two Existing Artificial Neural Network Models – Multilayer Perceptron and Probabilistic Neural Networks -- Beyond the Original PNN Model – Kernel Memory for Modeling Various Neural Pattern Processing Mechanism -- Modules within the Artificial Mind System and Their Interactions Relevant to Language Pattern Processing -- Concept Formation.
520 _aThis book proposes a novel connectionist approach to a challenging topic of language modeling within the context of kernel memory and artificial mind system, both proposed previously by the author in the very first volume of the series, Artificial Mind System—Kernel Memory Approach: Studies in Computational Intelligence, Vol. 1. The present volume focuses on how syntactic structures of language are modeled in terms of the respective composite connectionist architectures, each embracing both the nonsymbolic and symbolic parts. These two parts are developed via inter-module processes within the artificial mind system and eventually integrated under a unified framework of kernel memory. The data representation by the networks embodied within the kernel memory principle is essentially local, unlike conventional artificial neural network models such as the pervasive multilayer perceptron-based neural networks. With this locality principle, kernel memory inherently bears many attractive features, such as topologically unconstrained network formation, straightforward network growing, shrinking, and reconfiguration, no requirement of arduous iterative parameter tuning, construction of transparent and hierarchical data structures, and multimodal and temporal data processing via the network representation. Exploiting these multifacet properties of kernel memory with interweaving the notion of inter-module processing within the artificial mind system provides coherent accounts for concept formation and how various linguistic phenomena, viz. word compoundings, morphologies, and multiword constructions, are modeled. The description is then extended to more intricate network models of context-dependent lexical network and syntactic-oriented processing, the latter being the central theme of the present study, and further to those representing a hybrid of nonverbal and verbal thinking, and semantic and pragmatic aspects of sentential meaning. The book is intended for general readers engaging in various areas of study in cognitive science, computer science, engineering, linguistics, philosophy, psycholinguistics, and psychology.
650 0 _aEngineering
_xData processing.
650 0 _aComputational intelligence.
_964052
650 1 4 _aData Engineering.
_966059
650 2 4 _aComputational Intelligence.
_964052
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031573118
776 0 8 _iPrinted edition:
_z9783031573132
776 0 8 _iPrinted edition:
_z9783031573149
830 0 _aStudies in Computational Intelligence,
_x1860-9503 ;
_v1157
_968854
856 4 0 _uhttps://doi.org/10.1007/978-3-031-57312-5
912 _aZDB-2-INR
912 _aZDB-2-SXIT
999 _c60000
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