"Using support vector machines for classifying large sets of multi-represented objects"

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Book	Author(s)	Year	Publisher
Support Vector Machines	Ingo Steinwart; Andreas Christmann	2008	Springer New York
Learning to Classify Text Using Support Vector Machines	Thorsten Joachims	2002	Springer US
Learning to classify text using support vector machines	Thorsten Joachims (auth.)	2002	Springer US
Support Vector Machines Applications	Yunqian Ma; Guodong Guo	2014	Springer International Publishing
Support Vector Machines for Pattern Classification	Shigeo Abe	2005	Springer London
Support Vector Machines for Pattern Classification	Shigeo Abe	2nd ed. 2010	Springer London
Learning with Support Vector Machines	Colin Campbell; Yiming Ying		Springer International Publishing
Learning with support vector machines	Colin Campbell	2011	Morgan & Claypool
Least squares support vector machines /	Suykens, Johan A. K.	2002.	World Scientific,
Rule Extraction from Support Vector Machines	Joachim Diederich	2008	Springer Berlin Heidelberg
Support Vector Machines: Theory and Applications	Lipo Wang	2005	Springer Berlin Heidelberg
Knowledge Discovery with Support Vector Machines	Lutz Hamel	2009	John Wiley & Sons
Support-vector Machines: History and Applications	Pooja Saigal (editor)	2021	Nova Science Pub Inc
Support Vector Machines for Antenna Array Processing and Electromagnetics	Manel Martínez-Ramón; Christos Christodoulou		Springer International Publishing
Regularization, Optimization, Kernels, and Support Vector Machines	Johan A.K. Suykens; Marco Signoretto; Andreas Argyriou	2014	Routledge
Twin Support Vector Machines: Models, Extensions and Applications	Jayadeva; Reshma Khemchandani; Suresh Chandra	1st ed. 2017	Springer International Publishing
Support Vector Machines and Evolutionary Algorithms for Classification: Single or Together?	Catalin Stoean; Ruxandra Stoean	2014	Springer International Publishing
Support Vector Machines: Optimization Based Theory, Algorithms, and Extensions	Naiyang Deng; Yingjie Tian; Chunhua Zhang	2013	Routledge
Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets	Cengiz Kahraman; İrem Otay	1st ed. 2019	Springer International Publishing
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond	Bernhard Schölkopf; Alexander J. Smola	2001	MIT Press
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Chapter	Author(s)	Book	Book Authors	Year	Publisher
Using support vector machines for classifying large sets of multi-represented objects	Hans-Peter Kriegel, Peer Kroger, Alexej Pryakhin and Matthias Schubert	Proceedings of the Fourth SIAM International Conference on Data Mining /	Berry, Michael W.	[2004], ©2004.	Society for Industrial and Applied Mathematics,
Classifying Microarray Data Using Support Vector Machines	Sayan Mukherjee	A Practical Approach to Microarray Data Analysis	Daniel P. Berrar; Werner Dubitzky; Martin Granzow	2003	Springer US
Classifying income data using Support Vector Machines		Artificial Intelligence with Python	Prateek Joshi	2017	Packt Publishing
Classifying income data using Support Vector Machines		Artificial Intelligence with Python: Your complete guide to building intelligent apps using Python 3. x and TensorFlow 2	Alberto Artasanchez; Prateek Joshi	2020	Packt Publishing
Classifying income data using Support Vector Machines		Artificial intelligence with Python: build real-world artificial intelligence applications with Python to intelligently interact with the world around you /	Joshi, Prateek.	2017.	Packt Publishing,
Incremental Parallel Support Vector Machines for Classifying Large-Scale Multi-class Image Datasets	Thanh-Nghi Do; Minh-Thu Tran-Nguyen	Future Data and Security Engineering: Third International Conference, FDSE 2016, Can Tho City, Vietnam, November 23-25, 2016, Proceedings	Tran Khanh Dang; Roland Wagner; Josef Küng; Nam Thoai; Makoto Takizawa; Erich Neuhold	1st ed. 2016	Springer International Publishing
Using the Leader Algorithm with Support Vector Machines for Large Data Sets	Enrique Romero	Artificial Neural Networks and Machine Learning - ICANN 2011: 21st International Conference on Artificial Neural Networks, Espoo, Finland, June 14-17, 2011, Proceedings, Part I	Timo Honkela; Włodzisław Duch; Mark Girolami; Samuel Kaski	2011	Springer Berlin Heidelberg
Support Vector Machines using Multi Objective Programming	Takeshi Asada; Hirotaka Nakayama	Multi-Objective Programming and Goal Programming: Theory and Applications	Tetsuzo Tanino; Tamaki Tanaka; Masahiro Inuiguchi	2003	Springer Berlin Heidelberg
Classifying data with support vector machines		Building Data Science Applications with FastAPI: Develop, manage, and deploy efficient machine learning applications with Python	François Voron	2021	Packt Publishing
Using Support Vector Machines		Matlab. Statistics and Machine Learning Toolbox. User's Guide		2021	MathWorks
Using Support Vector Machines		MATLAB Statistics and Machine Learning Toolbox™ User's Guide	The MathWorks, Inc.	2020	The MathWorks, Inc.
Using support vector machines		40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python	Imran Ahmad	2020	Packt Publishing
Using support vector machines		40 Algorithms Every Programmer Should Know: Hone your problem-solving skills by learning different algorithms and their implementation in Python	I'm ran Ahmad	2022	Packt Publishing, Limited
Using support vector machines		Arduino Computer Vision Programming: Design and develop real-world computer vision applications with the powerful combination of OpenCV and Arduino	Ozen Ozkaya, Giray Yillikci	2015	Packt Publishing Limited
Multi-Layer Support Vector Machines	Marco A. Wiering and Lambert R.B. Schomaker	Regularization, Optimization, Kernels, and Support Vector Machines	Johan A.K. Suykens; Marco Signoretto; Andreas Argyriou	2014	Routledge
Multi-class support vector machines		Linguistic structure prediction	Noah A. Smith	2011	Morgan & Claypool
Combining Support Vector Machines for Classifying Fingerprint Images	The-Phi Pham; Minh-Thu Tran-Nguyen; Minh-Tan Tran; Thanh-Nghi Do	Future Data and Security Engineering: 7th International Conference, FDSE 2020, Quy Nhon, Vietnam, November 25–27, 2020, Proceedings	Tran Khanh Dang; Josef Küng; Makoto Takizawa; Tai M. Chung	1st ed. 2020	Springer International Publishing
Multi-represented kNN-Classification for Large Class Sets	Hans-Peter Kriegel; Alexey Pryakhin; Matthias Schubert	Database Systems for Advanced Applications: 10th International Conference, DASFAA 2005, Beijing, China, April 17-20, 2005, Proceedings	Lizhu Zhou; Beng Chin Ooi; Xiaofeng Meng	2005	Springer Berlin Heidelberg
Using Support Vector Machines and Rough Sets Theory for Classifying Faulty Types of Diesel Engine	Ping-Feng Pai; Yu-Ying Huang	Computational Science and Its Applications - ICCSA 2007: International Conference, Kuala Lumpur, Malaysia, August 26-29, 2007. Proceedings, Part I	Osvaldo Gervasi	2007	Springer Berlin Heidelberg
Training Support Vector Machines on Large Sets of Image Data	Ignas Kukenys; Brendan McCane; Tim Neumegen	Computer Vision -- ACCV 2009: 9th Asian Conference on Computer Vision, Xi' an, China, September 23-27, 2009, Revised Selected Papers, Part III	Hongbin Zha; Rin-ichiro Taniguchi; Stephen Maybank	2010	Springer Berlin Heidelberg
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