"Minimum Divergence Methods in Statistical Machine Learning: From an Information Geometric Viewpoint"

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Book	Author(s)	Year	Publisher
Minimum Divergence Methods in Statistical Machine Learning: From an Information Geometric Viewpoint	Shinto Eguchi; Osamu Komori	1st ed. 2022	Springer Japan
Statistical Methods for Machine Learning	Jason Brownlee	0
Information Fusion: Machine Learning Methods	Jinxing Li; Bob Zhang; David Zhang	1st ed. 2022	Springer Nature Singapore
Multivariate Statistical Machine Learning Methods for Genomic Prediction	Osval Antonio Montesinos López; Abelardo Montesinos López; José Crossa	1st ed. 2022	Springer International Publishing
Statistical Methods: The Geometric Approach	David J. Saville; Graham R. Wood	1991	Springer New York
Statistical Methods: A Geometric Primer	David J. Saville; Graham R. Wood	1996	Springer New York
Statistical methods: a geometric primer /	Saville, David J.; Wood, Graham R.	[1996], ©1996.	Springer,
Control Theory from the Geometric Viewpoint	Andrei A. Agrachev; Yuri Sachkov	2010	Springer
Control theory from the geometric viewpoint	Agrachev A. A., Sachkov Yu. N.	2004	Springer
Control Theory from the Geometric Viewpoint	Andrei A. Agrachev, Yuri L. Sachkov (auth.)	2004	Springer-Verlag Berlin Heidelberg
Control Theory from the Geometric Viewpoint	Andrei A. Agrachev; Yuri L. Sachkov	2004	Springer Berlin Heidelberg
Data Science and Machine Learning: Mathematical and Statistical Methods	Dirk P. Kroese; Zdravko I. Botev; Thomas Taimre; Radislav Vaisman	2019	Routledge
Complex Functions: An Algebraic and Geometric Viewpoint	Gareth A. Jones; David Singerman	1987-03	Cambridge University Press
Introduction to Statistical Machine Learning	Masashi Sugiyama	2016	Morgan Kaufmann
Statistical Reinforcement Learning: Modern Machine Learning Approaches	Masashi Sugiyama	2015	Routledge
Complex analysis: the geometric viewpoint	Steven G. Krantz; John P. D'Angelo	2004	Mathematical Association of America
Complex analysis: the geometric viewpoint	Steven G. Krantz; John P. D'Angelo	1990	Mathematical Association of America
Statistical Machine Learning: A Unified Framework	Richard Golden	2020	Routledge
Machine learning, neural and statistical classification /	Michie, Donald.; Spiegelhalter, D. J.; Taylor, C. C.,	1994.	Prentice Hall,
Kernel Methods and Machine Learning	S. Y. Kung	2014-04	Cambridge University Press
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Information Divergence Geometry and the Application to Statistical Machine Learning	Shinto Eguchi	Information Theory and Statistical Learning	Frank Emmert-Streib; Matthias Dehmer	2009	Springer US
Machine Learning and Statistical MAP Methods	Mark Kon; Leszek Plaskota; Andrzej Przybyszewski	Intelligent Information Processing and Web Mining: Proceedings of the International IIS: IIPWM´05 Conference held in Gdansk, Poland, June 13-16, 2005	Mieczyslaw A. Klopotek; Slawomir T. Wierzchon; Krzysztof Trojanowski	2005	Springer Berlin Heidelberg
Statistical Machine Learning	Masashi Sugiyama	Introduction to Statistical Machine Learning	Masashi Sugiyama	2016	Morgan Kaufmann
Statistical (Machine) Learning	Andrew F. Siegel; Michael R. Wagner	Practical Business Statistics(Eighth Edition)	Andrew Siegel andMichael Wagner	2022	Academic Press
Statistical machine learning		Practical statistics for data scientists: 50 essential concepts /	Bruce, Peter C.,; Bruce, Andrew,		O'Reilly Media, Inc.
Statistical machine learning		Machine Learning for Signal Processing: Data Science, Algorithms, and Computational Statistics	Max A. Little	2019	Oxford University Press
Geometric Optimization in Machine Learning	Suvrit Sra; Reshad Hosseini	Algorithmic Advances in Riemannian Geometry and Applications: For Machine Learning, Computer Vision, Statistics, and Optimization	Hà Quang Minh; Vittorio Murino	1st ed. 2016	Springer International Publishing
Geometric problems in machine learning	David Dobkin; Dimitrios Gunopulos	Applied Computational Geometry. Towards Geometric Engineering: FCRC ' 96 Workshop, WACG ' 96, Philadelphia, PA, May 27 - 28, 1996, Selected Papers	Ming C. Lin; Dinesh Manocha	1996	Springer Berlin Heidelberg
Statistical methods in learning	A. Sutherland; R. Henery; R. Molina; C. C. Taylor; R. King	IPMU' 92 - Advanced Methods in Artificial Intelligence: 4th International Conference on Information Processing and Management of Uncertainty in Knowledge-Based Systems, Palma de Mallorca, Spain, July 6-10, 1992. Proceedings	Bernadette Bouchon-Meunier; Llorenç Valverde; Ronald R. Yager	1993	Springer Berlin Heidelberg
Statistical learning methods		Deformable surface 3D reconstruction from monocular images	Mathieu Salzmann	2011	Morgan & Claypool
Statistical Learning Methods	Franck Vermet	Big Data for Insurance Companies	Marine Corlosquet-Habart; Jacques Janssen	2018	John Wiley & Sons
Pattern Learning and Recognition on Statistical Manifolds: An Information-Geometric Review	Frank Nielsen	Similarity-Based Pattern Recognition: Second International Workshop, SIMBAD 2013, York, UK, July 3-5, 2013, Proceedings	Edwin Hancock; Marcello Pelillo	2013	Springer Berlin Heidelberg
Machine learning methods		Handbook of Computational Social Science, Volume 2: Data Science, Statistical Modelling, and Machine Learning Methods	Uwe Engel; Anabel Quan-Haase; Sunny Liu; Lars Lyberg	2021	Routledge
Machine Learning Methods		Supervised Machine Learning for Text Analysis in R	Emil Hvitfeldt; Julia Silge	2021	Routledge
Machine Learning Methods	Liu Chu	Uncertainty Quantification of Stochastic Defects in Materials	Liu Chu	2021	Routledge
Machine Learning Methods		Survival Analysis	H J Vaman; Prabhanjan Tattar	2022	Routledge
Machine Learning Methods	Shyamasree Ghosh; Rathi Dasgupta	Machine Learning in Biological Sciences: Updates and Future Prospects	Shyamasree Ghosh; Rathi Dasgupta	1st ed. 2022	Springer Nature Singapore
Machine Learning Methods		Practical Synthetic Data Generation: Balancing Privacy and the Broad Availability of Data	Khaled El Emam, Lucy Mosquera, Richard Hoptroff	2020	O'Reilly Media
Machine learning methods		Intrusion detection: a machine learning approach	Zhenwei Yu	2011	Imperial College Press;Distributed by World Scientific
Machine learning methods		Компьютерная лингвистика и интеллектуальные технологии: По материалам ежегодной Международной конференции «Диалог» (2013)	Владимир П. Селегей и др. (ред.)	2013
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