Supervised and Unsupervised Learning for Data Science

by Michael W. Berry (Editor)

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

196 pages, Paperback

Published September 7, 2019