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Supervised and Unsupervised Learning for Data Science

Overview of attention for book
Cover of 'Supervised and Unsupervised Learning for Data Science'

Table of Contents

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    Book Overview
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    Chapter 1 A Systematic Review on Supervised and Unsupervised Machine Learning Algorithms for Data Science
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    Chapter 2 Overview of One-Pass and Discard-After-Learn Concepts for Classification and Clustering in Streaming Environment with Constraints
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    Chapter 3 Distributed Single-Source Shortest Path Algorithms with Two-Dimensional Graph Layout
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    Chapter 4 Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling
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    Chapter 5 Survival Support Vector Machines: A Simulation Study and Its Health-Related Application
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    Chapter 6 Semantic Unsupervised Learning for Word Sense Disambiguation
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    Chapter 7 Enhanced Tweet Hybrid Recommender System Using Unsupervised Topic Modeling and Matrix Factorization-Based Neural Network
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    Chapter 8 New Applications of a Supervised Computational Intelligence (CI) Approach: Case Study in Civil Engineering
Attention for Chapter 4: Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling
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Citations

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Summary Dimensions citations
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Chapter title
Using Non-negative Tensor Decomposition for Unsupervised Textual Influence Modeling
Chapter number 4
Book title
Supervised and Unsupervised Learning for Data Science
Published by
Springer, Cham, January 2020
DOI 10.1007/978-3-030-22475-2_4
Book ISBNs
978-3-03-022474-5, 978-3-03-022475-2
Authors

Robert E. Lowe, Michael W. Berry, Lowe, Robert E., Berry, Michael W.

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Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 2 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 2 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 1 50%
Student > Master 1 50%
Readers by discipline Count As %
Linguistics 1 50%
Computer Science 1 50%

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