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<div>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.<br><br>Topics of interest include:<br>- Unsupervised/Semi-Supervised Deep Learning<br>- Unsupervised/Semi-Supervised Discretization<br>- Unsupervised/Semi-Supervised Feature Extraction<br>- Unsupervised/Semi-Supervised Feature Selection<br>- Association Rule Learning<br>- Semi-Supervised Classification<br>- Semi-Supervised Regression<br>- Unsupervised/Semi-Supervised Clustering<br>- Unsupervised/Semi-Supervised Anomaly/Novelty/Outlier Detection<br>- Evaluation of Unsupervised/Semi-Supervised Learning Algorithms<br>- Applications of Unsupervised/Semi-Supervised Learning<br><br>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.<br><br>Indexed in: zbMATH Open (formerly known as Zentralblatt MATH)<br>Electronic ISSN: 2522-8498<br>Print ISSN: 2522-848X<br>Series Editor: M. Emre Celebi, Ph.D. (<a href="mailto:ecelebi@uca.edu">ecelebi@uca.edu</a>)<br>Publishing Editor: Mary James (<a href="mailto:mary.james@springer.com">mary.james@springer.com</a>)</div><div>Series Homepage: <a href="https://www.springer.com/series/15892">https://www.springer.com/series/15892</a></div><div>Book Proposal Form: <a href="https://media.springer.com/full/springer-instructions-for-authors-assets/pdf/SN_BPF_EN.pdf">https://media.springer.com/full/springer-instructions-for-authors-assets/pdf/SN_BPF_EN.pdf</a><br><br>Book Titles in This Series (16)<br>- Feature and Dimensionality Reduction for Clustering with Deep Learning (Frederic Ros & Rabia Riad, 2024)<br>- Partitional Clustering via Nonsmooth Optimization, Second Edition (Adil Bagirov, Napsu Karmitsa & Sona Taheri, 2025)<br>- Super-Resolution for Remote Sensing (Michal Kawulok, Jolanta Kawulok, Bogdan Smolka & M. Emre Celebi, 2024)<br>- Unsupervised Feature Extraction Applied to Bioinformatics, Second Edition (Y-h. Taguchi, 2024) <br>-
Advances in Computational Logistics and Supply Chain Analytics
(Ibraheem Alharbi, Chiheb-Eddine Ben Ncir, Bader Alyoubi & Hajer
Ben-Romdhane, 2024)<br>- Machine Learning and Data Analytics for Solving
Business Problems (Bader Alyoubi, Chiheb-Eddine Ben Ncir, Ibraheem
Alharbi & Anis Jarboui, 2022)<br>- Hidden Markov Models and Applications (Nizar Bouguila, Wentao Fan & Manar Amayri, 2022)<br>- Deep Biometrics (Richard Jiang, Chang-Tsun Li, Danny Crookes, Weizhi Meng & Christophe Rosenberger, 2020)<br>- Partitional Clustering via Nonsmooth Optimization (Adil M. Bagirov, Napsu Karmitsa & Sona Taheri, 2020)<br>- Sampling Techniques for Supervised or Unsupervised Tasks (Frédéric Ros & Serge Guillaume, 2020)<br>- Supervised and Unsupervised Learning for Data Science (Michael W. Berry, Azlinah Mohamed & Bee Wah Yap, 2020)<br>- Unsupervised Feature Extraction Applied to Bioinformatics (Y-h. Taguchi, 2020)<br>- Mixture Models and Applications (Nizar Bouguila & Wentao Fan, 2020)<br>- Linking and Mining Heterogeneous and Multi-view Data (Deepak P & Anna Jurek-Loughrey, 2019)<br>- Clustering Methods for Big Data Analytics (Olfa Nasraoui & Chiheb-Eddine Ben N'Cir, 2019)<br>- Natural Computing for Unsupervised Learning (Xiangtao Li & Ka-Chun Wong, 2019)</div>
<br clear="all"></div><div><div dir="ltr" class="gmail_signature" data-smartmail="gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><font size="2"><span style="font-size:10pt">--<br>M. Emre Celebi, Ph.D., Fellow SPIE</span></font></div><div dir="ltr"><div style="color:rgb(34,34,34)">he/him/his</div><font size="2"><span style="font-size:10pt">
Professor and Chair<br>
Department of Computer Science and Engineering</span></font></div><div dir="ltr"><span style="font-size:13.3333px">College of Science and Engineering</span><br><span style="font-size:10pt">University of Central Arkansas</span></div><div dir="ltr"><font size="2"><span style="font-size:10pt"><br></span></font></div><div dir="ltr"><span><span style="background-color:rgb(255,255,255)"><font color="#674ea7"><b>AVID</b>: UCA dedicates itself to <b>A</b>cademic <b>V</b>itality, <b>I</b>ntegrity, and <b>D</b>iversity</font></span>.</span></div><div dir="ltr"><font size="2"><span style="font-size:10pt"></span></font></div><font size="2"><span style="font-size:10pt"><br></span></font></div><div><font size="2"><span style="font-size:10pt">Office (Phone #): MCS 305 (501-852-0931)<br></span></font></div><div><font size="2"><span style="font-size:10pt"></span></font><div dir="ltr"><font size="2"><span style="font-size:10pt">Homepage: <a href="http://faculty.uca.edu/ecelebi/" target="_blank">http://faculty.uca.edu/ecelebi/</a><br></span></font></div></div><div><font size="2">GS: </font><a href="http://scholar.google.com/citations?user=mUzfrV8AAAAJ&hl=en" target="_blank">http://scholar.google.com/citations?user=mUzfrV8AAAAJ&hl=en</a></div><div></div><div><div><span><span style="background-color:rgb(255,255,255)"><font color="#674ea7"><b><br></b></font></span></span></div><div><div><img src="https://ci3.googleusercontent.com/mail-sig/AIorK4yFJqi2NzUoLh-yH8jOIdZlZwEVmakHMvl88YUQ-gmX8_-QAUrblgd4HMosq3E2ihn4nMuiwBs" width="96" height="18"></div></div></div></div></div></div></div></div></div>