Information Theory and Machine Learning

By:

Zheng, Lizhong [edt]

Contributor(s):

Material type: Article

ArticleLanguage: English Publication details: MDPI - Multidisciplinary Digital Publishing Institute 2022Description: 1 electronic resource (254 p.)ISBN:

9783036553085
9783036553078
9783036553085

Subject(s):

Компьютерная инженерия

Online resources:

Summary: The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.

List(s) this item appears in: Faculty Informational Technology

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Holdings
Item type	Current library	Collection	Shelving location	Call number	Status	Notes	Date due	Barcode
Electronic edition	Bucheon University Library	Computers	DOAB	004 I-60	Not for loan	Смотреть (pdf)		1010703

Open Access star Unrestricted online access

The recent successes of machine learning, especially regarding systems based on deep neural networks, have encouraged further research activities and raised a new set of challenges in understanding and designing complex machine learning algorithms. New applications require learning algorithms to be distributed, have transferable learning results, use computation resources efficiently, convergence quickly on online settings, have performance guarantees, satisfy fairness or privacy constraints, incorporate domain knowledge on model structures, etc. A new wave of developments in statistical learning theory and information theory has set out to address these challenges. This Special Issue, "Machine Learning and Information Theory", aims to collect recent results in this direction reflecting a diverse spectrum of visions and efforts to extend conventional theories and develop analysis tools for these complex machine learning systems.

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English

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