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Granular Computing

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27.07.2017 | Original Paper

DGCC: data-driven granular cognitive computing

verfasst von: Guoyin Wang

Erschienen in: Granular Computing | Ausgabe 4/2017

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Abstract

2016 is the 60 anniversary of the birth of artificial intelligence (AI). In the past 60 years, many AI theoretical models have been proposed and great achievements have been accomplished. Most intelligent computing models are inspired by various human/ natural/social intelligence mechanisms. Three main schools of artificial intelligence are formed, that is, symbolism, connectionism and behaviorism. Artificial intelligence is intelligence exhibited by machines. It is applied when a machine mimics cognitive functions that humans associate with other human minds. Achievements of cognitive science could give much inspiration to AI. Cognitive computing is one of the core fields of artificial intelligence. It aims to develop a coherent, unified, universal mechanism inspired by human mind’s capabilities. It is one of the most critical tasks for artificial intelligence researchers to develop advanced cognitive computing models. Cognitive computing is the third and most transformational phase in computing’s evolution, after the two distinct eras of computing—the tabulating era and the programming era. Inspired by human’s granularity thinking, problem solving mechanism and the cognition law of “global precedence”, a new powerful cognitive computing model, data-driven granular cognitive computing (DGCC), is proposed in this paper. It takes data as a special kind of knowledge expressed in the lowest granularity level of a multiple granularity space. It integrates two contradictory mechanisms, namely, the human’s cognition mechanism of “global precedence” which is a cognition process of “from coarser to finer” and the information processing mechanism of machine learning systems which is “from finer to coarser”, in a multiple granularity space. It is also based on the idea of data-driven. The research issues of DGCC to be further addressed are discussed. Based on DGCC, deep learning is neither classified into symbolism, nor connectionism. It is taken as a combination of symbolism and connectionism, and named hierarchical structuralism in this paper. The HD\(^3\) characteristics (hierarchical, distributed, data-driven, and dynamical) of the hierarchical structuralism are analyzed. DGCC provides a granular cognitive computing framework for efficient knowledge discovery from big data.

Vorheriger Artikel Probabilistic linguistic TODIM approach for multiple attribute decision-making

Nächster Artikel Semi-random partitioning of data into training and test sets in granular computing context

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Titel: DGCC: data-driven granular cognitive computing
verfasst von: Guoyin Wang
Publikationsdatum: 27.07.2017
Verlag: Springer International Publishing
Erschienen in: Granular Computing / Ausgabe 4/2017
Print ISSN: 2364-4966
Elektronische ISSN: 2364-4974
DOI: https://doi.org/10.1007/s41066-017-0048-3

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