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Unsupervised and Semi-Supervised Learning

Unsupervised and Semi-Supervised Learning
7 Volumes | 2019 - 2020

Description

Springer’s Unsupervised and Semi-Supervised Learning book series covers the latest theoretical and practical developments in unsupervised and semi-supervised learning. Titles -- including monographs, contributed works, professional books, and textbooks -- tackle various issues surrounding the proliferation of massive amounts of unlabeled data in many application domains and how unsupervised learning algorithms can automatically discover interesting and useful patterns in such data. The books discuss how these algorithms have found numerous applications including pattern recognition, market basket analysis, web mining, social network analysis, information retrieval, recommender systems, market research, intrusion detection, and fraud detection. Books also discuss semi-supervised algorithms, which can make use of both labeled and unlabeled data and can be useful in application domains where unlabeled data is abundant, yet it is possible to obtain a small amount of labeled data.

Topics of interest in include:

- Unsupervised/Semi-Supervised Discretization

- Unsupervised/Semi-Supervised Feature Extraction

- Unsupervised/Semi-Supervised Feature Selection

- Association Rule Learning

- Semi-Supervised Classification

- Semi-Supervised Regression

- Unsupervised/Semi-Supervised Clustering

- Unsupervised/Semi-Supervised Anomaly/Novelty/Outlier Detection

- Evaluation of Unsupervised/Semi-Supervised Learning Algorithms

- Applications of Unsupervised/Semi-Supervised Learning

While the series focuses on unsupervised and semi-supervised learning, outstanding contributions in the field of supervised learning will also be considered. The intended audience includes students, researchers, and practitioners.

All books of the series Unsupervised and Semi-Supervised Learning

2020 | Book

Mixture Models and Applications

This book focuses on recent advances, approaches, theories and applications related to mixture models. In particular, it presents recent unsupervised and semi-supervised frameworks that consider mixture models as their main tool. The chapters …

2020 | Book

Unsupervised Feature Extraction Applied to Bioinformatics

A PCA Based and TD Based Approach

This book proposes applications of tensor decomposition to unsupervised feature extraction and feature selection. The author posits that although supervised methods including deep learning have become popular, unsupervised methods have their own …

2020 | Book

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 …

2020 | Book

Sampling Techniques for Supervised or Unsupervised Tasks

This book describes in detail sampling techniques that can be used for unsupervised and supervised cases, with a focus on sampling techniques for machine learning algorithms. It covers theory and models of sampling methods for managing scalability …

2019 | Book

Clustering Methods for Big Data Analytics

Techniques, Toolboxes and Applications

This book highlights the state of the art and recent advances in Big Data clustering methods and their innovative applications in contemporary AI-driven systems. The book chapters discuss Deep Learning for Clustering, Blockchain data clustering …

2019 | Book

Natural Computing for Unsupervised Learning

This book highlights recent research advances in unsupervised learning using natural computing techniques such as artificial neural networks, evolutionary algorithms, swarm intelligence, artificial immune systems, artificial life, quantum …

2019 | Book

Linking and Mining Heterogeneous and Multi-view Data

This book highlights research in linking and mining data from across varied data sources. The authors focus on recent advances in this burgeoning field of multi-source data fusion, with an emphasis on exploratory and unsupervised data analysis, an …