Supervised and Unsupervised Learning for Data Science /

This book covers the state of the art in learning algorithms with an inclusion of semi-supervised methods to provide a broad scope of clustering and classification solutions for big data applications. Case studies and best practices are included along with theoretical models of learning for a compre...

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Bibliographic Details
Corporate Author:	SpringerLink (Online service)
Other Authors:	Berry, Michael W. (Editor), Mohamed, Azlinah (Editor), Yap, Bee Wah (Editor)
Format:	eBook
Language:	English
Published:	Cham : Springer International Publishing : Imprint: Springer, 2020.
Edition:	1st ed. 2020.
Series:	Unsupervised and Semi-Supervised Learning,
Subjects:	Electrical engineering. Signal processing. Image processing. Speech processing systems. Pattern recognition. Artificial intelligence. Data mining. Communications Engineering, Networks. Signal, Image and Speech Processing. Pattern Recognition. Artificial Intelligence. Data Mining and Knowledge Discovery. Electronic books.
Online Access:	Connect to the full text of this electronic book

Table of Contents:

Chapter1: A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
Chapter2: Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints
Chapter3: Distributed Single-Source Shortest Path Algorithms with Two Dimensional Graph Layout
Chapter4: Using Non-Negative Tensor Decomposition for Unsupervised Textual Influence Modeling
Chapter5: Survival Support Vector Machines: A Simulation Study and Its Health-related Application
Chapter6: Semantic Unsupervised Learning for Word Sense Disambiguation
Chapter7: Enhanced Tweet Hybrid Recommender System using Unsupervised Topic Modeling and Matrix Factorization based Neural Network
Chapter8: New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering.