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Machine learningtheoretical foundati...

Pandey, Manjusha.

Machine learningtheoretical foundations and practical applications /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine learningedited by Manjusha Pandey, Siddharth Swarup Rautaray.
其他題名:	theoretical foundations and practical applications /
其他作者:	Pandey, Manjusha.
出版者:	Singapore :Springer Singapore :2021.
面頁冊數:	xi, 172 p. :ill. (some col.), digital ;24 cm.
Contained By:	Springer Nature eBook
標題:	Machine learningCongresses.
電子資源:	https://doi.org/10.1007/978-981-33-6518-6
ISBN:	9789813365186$q(electronic bk.)

Machine learningtheoretical foundations and practical applications /
Machine learningtheoretical foundations and practical applications /[electronic resource] :edited by Manjusha Pandey, Siddharth Swarup Rautaray. - Singapore :Springer Singapore :2021. - xi, 172 p. :ill. (some col.), digital ;24 cm. - Studies in big data,v.872197-6503 ;. - Studies in big data ;v.1..

Chapter 1. What do RDMs capture in Brain Responses and Computational Models? -- Chapter 2. Challenges and solutions, in developing Convolutional Neural Networks and Long Short Term Memory networks, for industry problems -- Chapter 3. Speed, Cloth and Pose Invariant Gait recognition Based Person Identifification -- Chapter 4. Applications of Machine learning in industry 4.0 -- Chapter 5. Web Semantics and Knowledge Graph -- Chapter 6. Machine Learning based Wireless Sensor Networks -- Chapter 7. AI to Machine Learning:lifeless automation and Issues -- Chapter 8. Analysis of FDIs in Different Sectors of the Indian Economy -- Chapter 9. Customer Profiling & Retention using Recommendation system and Factor Identification to predict Customer Chur In Telecom Industry.

This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9-12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.

ISBN: 9789813365186$q(electronic bk.)

Standard No.: 10.1007/978-981-33-6518-6doiSubjects--Topical Terms:

384498
Machine learning
--Congresses.

LC Class. No.: Q325.5 / .M334 2021

Dewey Class. No.: 006.31

Machine learningtheoretical foundations and practical applications /
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505 0 $a Chapter 1. What do RDMs capture in Brain Responses and Computational Models? -- Chapter 2. Challenges and solutions, in developing Convolutional Neural Networks and Long Short Term Memory networks, for industry problems -- Chapter 3. Speed, Cloth and Pose Invariant Gait recognition Based Person Identifification -- Chapter 4. Applications of Machine learning in industry 4.0 -- Chapter 5. Web Semantics and Knowledge Graph -- Chapter 6. Machine Learning based Wireless Sensor Networks -- Chapter 7. AI to Machine Learning:lifeless automation and Issues -- Chapter 8. Analysis of FDIs in Different Sectors of the Indian Economy -- Chapter 9. Customer Profiling & Retention using Recommendation system and Factor Identification to predict Customer Chur In Telecom Industry.
520 $a This edited book is a collection of chapters invited and presented by experts at 10th industry symposium held during 9-12 January 2020 in conjunction with 16th edition of ICDCIT. The book covers topics, like machine learning and its applications, statistical learning, neural network learning, knowledge acquisition and learning, knowledge intensive learning, machine learning and information retrieval, machine learning for web navigation and mining, learning through mobile data mining, text and multimedia mining through machine learning, distributed and parallel learning algorithms and applications, feature extraction and classification, theories and models for plausible reasoning, computational learning theory, cognitive modelling and hybrid learning algorithms.
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