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...
| Other Authors: | , , |
|---|---|
| Format: | eBook |
| Language: | English |
| Published: |
Cham
Springer International Publishing
2020, 2020
|
| Edition: | 1st ed. 2020 |
| Series: | Unsupervised and Semi-Supervised Learning
|
| Subjects: | |
| Online Access: | |
| Collection: | Springer eBooks 2005- - Collection details see MPG.ReNa |
Table of Contents:
- Chapter1: A Systematic Review on Supervised & 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