Titlebook: Syntactic Networks—Kernel Memory Approach; Tetsuya Hoya Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusive license

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Tetsuya Hoya field of Social Innovation. Thereby the authors not only develop a comprehensive and unique analysis on the state-of-the art of social innovation but also give practical advice and information to business leaders on how to apply the latest management thinking on Social Innovation to daily business

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Introduction,ile the significance of the probabilistic neural network model has yet to be discovered by the community, the model has great potential to provide robust and straightforward approaches to various problems of interest in the pattern classification domain. This chapter, therefore, reveals the benefici

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,Beyond the Original PNN Model—Kernel Memory for Modeling Various Neural Pattern Processing Mechanisrmed. The localist approach also enables performing incremental learning tasks without the utility of an iterative parameter optimization algorithm, as required in conventional multilayer perceptron neural network models. Although the notion of learning in the context of conventional artificial neur

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Book 2024ased 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 t

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Syntactic Networks—Kernel Memory Approach978-3-031-57312-5Series ISSN 1860-949X Series E-ISSN 1860-9503

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Syntactic Processing,uistic variables, play a significant role in the syntactic processing of a language. The last four sections of the chapter are devoted to the issues beyond genuine syntactic processing, i.e., the semantic and pragmatic aspects of language, both generally intangible to be dealt with.