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Machine Learning: Theoretical Foundations and Practical Applications

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	Machine Learning: Theoretical Foundations and Practical Applications/ edited by Manjusha Pandey, Siddharth Swarup Rautaray.
其他作者:	Rautaray, Siddharth Swarup.
面頁冊數:	XI, 172 p. 71 illus., 55 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Communications Engineering, Networks. -
電子資源:	https://doi.org/10.1007/978-981-33-6518-6
ISBN:	9789813365186

Machine Learning: Theoretical Foundations and Practical Applications
Machine Learning: Theoretical Foundations and Practical Applications[electronic resource] /edited by Manjusha Pandey, Siddharth Swarup Rautaray. - 1st ed. 2021. - XI, 172 p. 71 illus., 55 illus. in color.online resource. - Studies in Big Data,872197-6511 ;. - Studies in Big Data,8.

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

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

669809
Communications Engineering, Networks.

LC Class. No.: Q342

Dewey Class. No.: 006.3

Machine Learning: Theoretical Foundations and Practical Applications
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