书目名称 | Syntactic Networks—Kernel Memory Approach | 编辑 | Tetsuya Hoya | 视频video | | 概述 | Focuses upon providing a framework to model a composite network system.Proposes a novel connectionist approach to a challenging topic of language modeling.Presents a conceptual framework of kernel mem | 丛书名称 | Studies in Computational Intelligence | 图书封面 |  | 描述 | .This 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 t | 出版日期 | Book 2024 | 关键词 | Computational Intelligence; Data Engineering; Kernel Memory; Syntactic Networks; Artificial Mind System | 版次 | 1 | doi | https://doi.org/10.1007/978-3-031-57312-5 | isbn_softcover | 978-3-031-57314-9 | isbn_ebook | 978-3-031-57312-5Series ISSN 1860-949X Series E-ISSN 1860-9503 | issn_series | 1860-949X | copyright | The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl |
The information of publication is updating
|
|