£94.21

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Springer Machine Learning: Theoretical Foundations and Practical Applications - Book

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Description

This comprehensive edited collection brings together expert contributions from the 10th industry symposium held in January 2020, presented alongside the 16th edition of ICDCIT. The book explores machine learning across multiple dimensions, including statistical learning theory, neural network architectures, knowledge acquisition methodologies, and knowledge-intensive learning systems. It covers practical applications of machine learning in information retrieval, web navigation, and data mining techniques. The volume addresses emerging areas such as mobile data mining, text and multimedia processing through machine learning, as well as distributed and parallel learning algorithms. Published by Springer as part of the Studies in Big Data series (Volume 87), this work provides both theoretical grounding and real-world applications for professionals and researchers working in artificial intelligence and data science fields.

Key Features

Edited collection featuring expert contributions from the 10th industry symposium held during January 2020.

Covers neural network learning, statistical learning theory, and knowledge acquisition methodologies essential for modern machine learning.

Explores machine learning applications in information retrieval, web navigation, and distributed data mining systems.

Addresses text and multimedia mining through machine learning, plus mobile data mining and parallel learning algorithms.

Published by Springer as part of the Studies in Big Data series, Volume 87, targeting researchers and professionals.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
21 April 2022
Listed Since
25 March 2022

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