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Machine Learning for Data Science Handbook
Editor
Listed:
- Lior Rokach(Ben-Gurion University of the Negev, Department of Software and Information Systems Engineering)Oded Maimon(Tel Aviv University, Department of Industrial Engineering)Erez Shmueli(Tel Aviv University, Department of Industrial Engineering)
Abstract
No abstract is available for this item.Individual chapters are listed in the "Chapters" tab
Suggested Citation
- Lior Rokach & Oded Maimon & Erez Shmueli (ed.), 2023. "Machine Learning for Data Science Handbook," Springer Books, Springer, edition 0, number 978-3-031-24628-9, March.
Handle: RePEc:spr:sprbok:978-3-031-24628-9
DOI: 10.1007/978-3-031-24628-9
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Book Chapters
The following chapters of this book are listed in IDEAS- Oded Maimon & Lior Rokach & Erez Shmueli, 2023. "Data Science and Knowledge Discovery Using Machine Learning Methods," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 1-19, Springer.
- Jerzy W. Grzymala-Busse & Witold J. Grzymala-Busse, 2023. "Handling Missing Attribute Values," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 21-38, Springer.
- Kartick Chandra Mondal & Swati Saha, 2023. "Data Integration Process Automation Using Machine Learning: Issues and Solution," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 39-54, Springer.
- Jerzy W. Grzymala-Busse, 2023. "Rule Induction," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 55-74, Springer.
- Aryeh Kontorovich & Samory Kpotufe, 2023. "Nearest-Neighbor Methods: A Modern Perspective," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 75-92, Springer.
- Armin Shmilovici, 2023. "Support Vector Machines," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 93-110, Springer.
- Boaz Lerner, 2023. "Empowering Interpretable, Explainable Machine Learning Using Bayesian Network Classifiers," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 111-142, Springer.
- Oren Fivel & Moshe Klein & Oded Maimon, 2023. "Soft Decision Trees," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 143-170, Springer.
- Amichai Painsky, 2023. "Quality Assessment and Evaluation Criteria in Supervised Learning," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 171-195, Springer.
- Yulong Wang & Yuan Yan Tang, 2023. "Trajectory Clustering Analysis," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 197-217, Springer.
- Michael E. Houle & Marie Kiermeier & Arthur Zimek, 2023. "Clustering High-Dimensional Data," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 219-237, Springer.
- Janmenjoy Nayak & H. Swapna Rekha & Bighnaraj Naik, 2023. "Fuzzy C-Means Clustering: Advances and Challenges (Part II)," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 239-269, Springer.
- Charu C. Aggarwal, 2023. "Clustering in Streams," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 271-300, Springer.
- Lihi Shiloh-Perl & Raja Giryes, 2023. "Introduction to Deep Learning," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 301-338, Springer.
- Palash Goyal, 2023. "Graph Embedding," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 339-351, Springer.
- Dor Bank & Noam Koenigstein & Raja Giryes, 2023. "Autoencoders," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 353-374, Springer.
- Gilad Cohen & Raja Giryes, 2023. "Generative Adversarial Networks," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 375-400, Springer.
- Yan Li & Yiqun Xie & Shashi Shekhar, 2023. "Spatial Data Science," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 401-422, Springer.
- Zhongfei Mark Zhang & Ruofei Bruce Zhang, 2023. "Multimedia Data Learning," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 423-446, Springer.
- Petar Ristoski, 2023. "Web Mining," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 447-467, Springer.
- Robert Moskovitch, 2023. "Mining Temporal Data," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 469-490, Springer.
- Hrishav Bakul Barua & Kartick Chandra Mondal, 2023. "Cloud Big Data Mining and Analytics: Bringing Greenness and Acceleration in the Cloud," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 491-510, Springer.
- Lihi Dery, 2023. "Multi-Label Ranking: Mining Multi-Label and Label Ranking Data," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 511-535, Springer.
- Jonatan Barkan & Michal Moran & Goren Gordon, 2023. "Reinforcement Learning for Data Science," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 537-557, Springer.
- Ziv Katzir & Yuval Elovici, 2023. "Adversarial Machine Learning," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 559-585, Springer.
- Yonatan Hadar & Erez Shmueli, 2023. "Ensembled Transferred Embeddings," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 587-606, Springer.
- Beatrice Amico & Carlo Combi & Yuval Shahar, 2023. "Data Mining in Medicine," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 607-636, Springer.
- Shuai Zhang & Aston Zhang & Lina Yao, 2023. "Recommender Systems," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 637-658, Springer.
- Jindong Wang & Yiqiang Chen & Chunyu Hu, 2023. "Activity Recognition," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 659-680, Springer.
- Aviad Elyashar & Maor Reuben & Asaf Shabtai & Rami Puzis, 2023. "Social Network Analysis for Disinformation Detection," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 681-701, Springer.
- Mark Last, 2023. "Online Propaganda Detection," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 703-719, Springer.
- Boris Kovalerchuk & Evgenii Vityaev & Alexander Demin & Antoni Wilinski, 2023. "Interpretable Machine Learning forFinancial Applications," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 721-749, Springer.
- Jacob Zahavi, 2023. "Predictive Analytics for Targeting Decisions," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 751-777, Springer.
- Neta Rabin & Yuri Bregman, 2023. "Machine Learning for the Geosciences," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 779-800, Springer.
- Nir Ofek, 2023. "Sentiment Analysis for Social Text," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 801-831, Springer.
- Hila Chalutz-Ben Gal, 2023. "Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 833-866, Springer.
- Dana Pessach & Erez Shmueli, 2023. "Algorithmic Fairness," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 867-886, Springer.
- Ron S. Hirschprung, 2023. "Privacy-Preserving Data Mining (PPDM)," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 887-911, Springer.
- Boris Kovalerchuk, 2023. "Explainable Machine Learning and Visual Knowledge Discovery," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 913-943, Springer.
- Salomon Eisler & Joachim Meyer, 2023. "Visual Analytics and Human Involvement in Machine Learning," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 945-970, Springer.
- Aviv Notovich & Hila Chalutz-Ben Gal & Irad Ben-Gal, 2023. "Explainable Artificial Intelligence (XAI): Motivation, Terminology, and Taxonomy," Springer Books, in: Lior Rokach & Oded Maimon & Erez Shmueli (ed.), Machine Learning for Data Science Handbook, edition 0, pages 971-985, Springer.
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