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Alpha seeding for support vector machines

Authors:

Dennis DeCoste,

Kiri WagstaffAuthors Info & Claims

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 345 - 349

https://doi.org/10.1145/347090.347165

Published: 01 August 2000 Publication History

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References

[1]

C. Burges. A tutorial on support vector machines for pattern recognition. Data Mining and Knowledge Discovery, 2(2), 1998.

Digital Library

Google Scholar

[2]

N. Cristianini, C. Campbell, and J. Shawe-Taylor. Dynamically adapting kernels in support vector machines. Technical Report NeuroCOLT Technical Report NC-TR-98-017, Royal Holloway College, University of London, May 1998.

Google Scholar

[3]

Dennis DeCoste and Michael Burl. Distortion-invariant recognition via jittered queries. In Computer Vision and Pattern Recognition (CVPR-2000), June 2000.

Crossref

Google Scholar

[4]

Isabelle Guyon. Online SVM application list. (See http://www.clopinet.com/isabelle/Projects/SVM/ applist.html.).

Google Scholar

[5]

T. Joachims. Making large-scale support vector machine learning practical, 1999. In Advances in Kernel Methods: Support Vector Machines {9}.

Digital Library

Google Scholar

[6]

S.S. Keerthi, S.K. Shevade, C. Bhattacharyya, and K.R.K. Murthy. Improvements to Platt's SMO algorithm for svm classifier design. Technical Report CD-99-14, Dept. of Mechanical and Production Engineering, National University of Singapore, 1999.

Google Scholar

[7]

John Platt. Fast training of support vector machines using sequential minimal optimization, 1999. In Advances in Kernel Methods: Support Vector Machines {9}.

Digital Library

Google Scholar

[8]

B. Sch. olkopf, A. Smola, and K.R. M. uller. Nonlinear component analysis as a kernel eigenvalue problem. Technical report no. 44, Max-Planck-Institut for Biologische Kybernetik, T. ubingen, Dec 1996.

Google Scholar

[9]

B. Schoelkopf, C. Burges, and A. Smola. Advances in Kernel Methods: Support Vector Machines. MIT Press, Cambridge, MA, 1999.

Digital Library

Google Scholar

[10]

V. Vapnik. The Nature of Statistical Learning Theory. Springer Verlag, New York, 1995.

Digital Library

Google Scholar

[11]

Jason Weston. Leave-on-out support vector machines. In Proceedings of International Joint Conference on Artificial Intelligence (IJCAI-99), 1999.

Digital Library

Google Scholar

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Ding XLi YChen S(2024)Maximum margin and global criterion based-recursive feature selectionNeural Networks10.1016/j.neunet.2023.10.037169(597-606)Online publication date: Jan-2024
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Ding XCui MLi YChen S(2024)A maximal accuracy and minimal difference criterion for multiple kernel learningExpert Systems with Applications10.1016/j.eswa.2024.124378254(124378)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124378
Ding XYang FMa F(2022)An Efficient Model Selection for Linear Discrimination Function-based Recursive Feature EliminationJournal of Biomedical Informatics10.1016/j.jbi.2022.104070(104070)Online publication date: Apr-2022
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Alpha seeding for support vector machines

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Published In

KDD '00: Proceedings of the sixth ACM SIGKDD international conference on Knowledge discovery and data mining

August 2000

537 pages

ISBN:1581132336

DOI:10.1145/347090

Chairmen:
Raghu Ramakrishnan
Univ. of Wisconsin
,
Sal Stolfo
Columbia Univ., New York, NY
,
Roberto Bayardo
IBM Almaden Research Center, San Jose, CA
,
Ismail Parsa
Epsilon

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Association for Computing Machinery

New York, NY, United States

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Published: 01 August 2000

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KDD00

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KDD00: The Second Annual International Conference on Knowledge Discovery in Data

August 20 - 23, 2000

Massachusetts, Boston, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Ding XLi YChen S(2024)Maximum margin and global criterion based-recursive feature selectionNeural Networks10.1016/j.neunet.2023.10.037169(597-606)Online publication date: Jan-2024
https://doi.org/10.1016/j.neunet.2023.10.037
Ding XCui MLi YChen S(2024)A maximal accuracy and minimal difference criterion for multiple kernel learningExpert Systems with Applications10.1016/j.eswa.2024.124378254(124378)Online publication date: Nov-2024
https://doi.org/10.1016/j.eswa.2024.124378
Ding XYang FMa F(2022)An Efficient Model Selection for Linear Discrimination Function-based Recursive Feature EliminationJournal of Biomedical Informatics10.1016/j.jbi.2022.104070(104070)Online publication date: Apr-2022
https://doi.org/10.1016/j.jbi.2022.104070
Khan MSwaroop SRanzato MBeygelzimer ADauphin YLiang PVaughan J(2021)Knowledge-adaptation priorsProceedings of the 35th International Conference on Neural Information Processing Systems10.5555/3540261.3541772(19757-19770)Online publication date: 6-Dec-2021
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Park HLee DPark SMin JShinn JLee DKwon SKim HKim N(2021)Development of a Predictive Model for Glycated Hemoglobin Values and Analysis of the Factors Affecting ItCardiovascular Prevention and Pharmacotherapy10.36011/cpp.2021.3.e143:4(106-114)Online publication date: 31-Oct-2021
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Ding XYang FJin SCao J(2021)An Efficient Alpha Seeding Method for Optimized Extreme Learning Machine-based Feature Selection AlgorithmComputers in Biology and Medicine10.1016/j.compbiomed.2021.104505(104505)Online publication date: May-2021
https://doi.org/10.1016/j.compbiomed.2021.104505
Ash JAdams RLarochelle HRanzato MHadsell RBalcan MLin H(2020)On warm-starting neural network trainingProceedings of the 34th International Conference on Neural Information Processing Systems10.5555/3495724.3496051(3884-3894)Online publication date: 6-Dec-2020
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Hsia JLin C(2020)Parameter Selection for Linear Support Vector RegressionIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2020.296763731:12(5639-5644)Online publication date: Dec-2020
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Touati ROueslati AMessaoudi ILachiri Z(2019)The Helitron family classification using SVM based on Fourier transform features applied on an unbalanced datasetMedical & Biological Engineering & Computing10.1007/s11517-019-02027-5Online publication date: 17-Aug-2019
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Juan YLefortier DChapelle OBarrett RCummings RAgichtein EGabrilovich E(2017)Field-aware Factorization Machines in a Real-world Online Advertising SystemProceedings of the 26th International Conference on World Wide Web Companion10.1145/3041021.3054185(680-688)Online publication date: 3-Apr-2017
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