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Machine Learning: Theoretical Foundations and Practical Applications / edited by Manjusha Pandey, Siddharth Swarup Rautaray.

Contributor(s): Pandey, Manjusha, editor literario | Rautaray, Siddharth Swarup, editor literario
Series: (Studies in Big Data, 2197-6511; 87); (Intelligent Technologies and Robotics (SpringerNature-42732)); (Intelligent Technologies and Robotics (R0) (SpringerNature-43728)).Publisher: Singapore : Springer International Pulishing, 2021Edition: First edition 2021.Description: 1 recurso en línea (XI, 172 páginas) : 71 ilustraciones, 55 ilustraciones a color.ISBN: 9789813365186.Subject: Aprendizaje automáticoOnline resources: Acceso a este recurso digital (usuarios Universidad Europea de Madrid)Digital Resources
Contents:
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.
Abstract: 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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Holdings
Item type Current library Collection Call number Status Date due Barcode Item holds
LIBRO-E NO PRÉSTAMO LIBRO-E NO PRÉSTAMO Madrid Digital Acceso Electrónico (UEM) Ciencias e Ingeniería Q325.5 (Browse shelf(Opens below)) Acceso electrónico eBook.23122151
Total holds: 0

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.

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