Electronic Resource

Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics

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The present reprint contains 33 articles accepted and published in the Special Issue entitled "Advancement of Mathematical Methods in Feature Representation Learning for Artificial Intelligence, Data Mining and Robotics, 2022" in the MDPI journal, Mathematics, which covers a wide range of topics connected to the theory and applications of feature representation learning for image processing, artificial intelligence, data mining and robotics. These topics include, among others, elements from image blurring, image aesthetic quality assessment, pedestrian detection, visual tracking, vehicle re-identification, face recognition, 3D reconstruction, the stability of switched systems, domain adaption, deep reinforcement, sentiment analysis, graph convolutional networks, knowledge graphs, geometric metric learning, etc. It is hoped that this reprint will be interesting and useful for those working in the area of image processing, computer vision, machine learning, natural language processing and robotics, as well as for those with backgrounds in machine learning who are willing to become familiar with recent advancements in artificial intelligence, which, today, is present in almost all aspects of human life and activities.

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Detail Information

Series Title

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Call Number

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Publisher

Basel, 2023 : MDPI - Multidisciplinary Digital Publishing Institute., 2023

Collation

556 hlm; ill., lamp.,

Language

English

ISBN/ISSN

9783036572635

Classification

NONE

Content Type

text

Media Type

computer

Carrier Type

online resource

Edition

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Subject(s)

Computer Science
Information technology industries

Specific Detail Info

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Statement of Responsibility

Gou, Jianping (editor) Ou, Weihua (editor) Zeng, Shaoning (editor) Du, Lan (editor)

Other Information

Cataloger

ida

Source

https://directory.doabooks.org/handle/20.500.12854/101363

Validator

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Digital Object Identifier (DOI)

10.3390/books978-3-0365-7263-5

Journal Volume

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Journal Issue

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Subtitle

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Parallel Title

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