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Robust Representation for Data Analytics: Models and Applications - Advanced Information and Knowledge Processing by Sheng Li Details

This book offers strong representation learning concepts and models, and provides a range of solutions for dealing with real-world data analysis tasks such as aggregation, classifying, time series modeling, outward…

Robust Representation for Data Analytics synopsis

This book offers strong representation learning concepts and models, and provides a range of solutions for dealing with real-world data analysis tasks such as aggregation, classifying, time series modeling, outward detection, collaborative filtering, community detection, etc. A variety of powerful feature representations, which extend the understanding of the graph, the micro-domain, and the dictionary, are developed.

Taking advantage of low-grade and sporadic modeling theory, authors develop strong representations of features within different learning models, including unattended learning, supervised learning , Semi-supervised education, multi-learning, learning transfer, and deep learning. "Strong representations of data analysis" covers a wide range of applications in the areas of large data research, human-centered computing, pattern recognition, digital marketing, web mining, and computer vision..



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