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 considers mi
- PDF / 10,488,831 Bytes
- 356 Pages / 439.42 x 683.15 pts Page_size
- 38 Downloads / 214 Views
Nizar Bouguila Wentao Fan Editors
Mixture Models and Applications
Unsupervised and Semi-Supervised Learning Series Editor M. Emre Celebi, Computer Science Department, Conway, Arkansas, USA
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.
More information about this series at http://www.springer.com/series/15892
Nizar Bouguila • Wentao Fan Editors
Mixture Models and Applications
123
Editors Nizar Bouguila Concordia Institute for Information Systems Engineering Concordia University QC, Montreal, Canada
Wentao Fan Department of Computer Science and Technology Huaqiao University Xiamen, China
ISSN 2522-848X ISSN 2522-8498 (electronic) Unsupervised and Semi-Supervised Learning ISBN 978-3-030-23875-9 ISBN 978-3-030-23876-6 (eBook) https://doi.org/10.1007/978-3-030-23876-6 © Springer Nature Switzerland AG 2020 This work is subject to copyright. All rights are reserved by the Publisher, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilms or in any other physical way, and transmission or information storage and retrieval, electronic adaptation, computer software, or by similar or dissimilar methodology now known or hereafter developed. The use of general descriptive names, registered names, trademarks, service marks, etc. in this publication doe
Data Loading...
