About this book series
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 include:
- Unsupervised/Semi-Supervised Deep Learning
- 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 supervised learning (e.g., deep learning) will also be considered. The intended audience includes students, researchers, and practitioners.
** Indexing: The books of this series indexed in zbMATH **
- Electronic ISSN
- 2522-8498
- Print ISSN
- 2522-848X
- Series Editor
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- M. Emre Celebi
Book titles in this series
Abstracted and indexed in
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- zbMATH