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https://dl.acm.org/citation.cfm?id=775073
We give a statistical interpretation of Proximal Support Vector Machines (PSVM) proposed at KDD2001 as linear approximaters to (nonlinear) Support Vector Machines (SVM). We prove that PSVM using a linear kernel is identical to ridge regression, a biased-regression method known in the statistical community for more than thirty years.Cited by: 30
https://www.researchgate.net/publication/2565943_Shrinkage_Estimator_Generalizations_of_Proximal_Support_Vector_Machines
Agarwal [48] has given a statistical interpretation of proximal support vector machines (PSVM) as linear approximates to (nonlinear) support vector machines and proved that PSVM using a linear ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.13.2376
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): We give a statistical interpretation of Proximal Support Vec- tor Machines (PSVM) proposed at KDD2001 as linear approximaters to (nonlinear) Support Vector Machines (SVM). We prove that PSVM using a linear kernel is identical to ridge regression, a biased-regression method known in the statistical community for …
https://static.aminer.org/pdf/PDF/000/472/382/shrinkage_estimator_generalizations_of_proximal_support_vector_machines.pdf
Shrinkage Estimator Generalizations of Proximal Support Vector Machines Deepak K. Agarwal AT&T Labs-Research 180 Park Avenue Florham Park, NJ 07932, USA
https://core.ac.uk/display/20934278
Shrinkage Estimator Generalizations of Proximal Support Vector Machines . By Deepak K. Agarwal. Abstract. We give a statistical interpretation of Proximal Support Vec- tor Machines (PSVM) proposed at KDD2001 as linear approximaters to (nonlinear) Support Vector Machines (SVM). We prove that PSVM using a linear kernel is identical to ridge ...Cited by: 30
https://link.springer.com/article/10.1007%2Fs00778-006-0002-5
Aug 31, 2006 · Agarwal, D.K.: Shrinkage estimator generalizations of proximal support vector machines, In: Proceedings of the 8th International Conference Knowledge Discovery and Data Mining, pp. 173–182.Cited by: 433
https://dl.acm.org/citation.cfm?id=502527
Deepak K. Agarwal, Shrinkage estimator generalizations of Proximal Support Vector Machines, Proceedings of the eighth ACM SIGKDD international conference on Knowledge discovery and data mining, July 23-26, 2002, Edmonton, Alberta, CanadaCited by: 1067
http://pages.cs.wisc.edu/~gfung/
Related Paper: Shrinkage Estimator Generalizations of Proximal Support Vector Machines (pdf), D.K. Agarwal and William DuMouchel, ATT research Labs. Presented at KDD 2002. Glenn Fung, O. L. Mangasarian & Alexander Smola. Minimal Kernel Classifiers.PDF Version
https://www.sciencedirect.com/science/article/pii/S1875389212015568
Machine learning in automated text categorisation. Technical Report IEI-B4-31-1999, Istituto di Elaborazione dell’Informazione, 2001:22-53 [6] Agarwal, D.K.: Shrinkage estimator generalizations of proximal support vector machines,The 8th International Conference Knowledge Discovery and DataMining, pp.173–182.Cited by: 2
https://www.researchgate.net/publication/251842652_A_Novel_Proximal_Support_Vector_Machine_and_Its_Application_in_Radar_Target_Recognition
Shrinkage Estimator Generalizations of Proximal Support Vector Machines// Proceedings of the 8th ACM SIGKDD International Conference On Knowledge Discovery and …
https://www.researchgate.net/publication/251842652_A_Novel_Proximal_Support_Vector_Machine_and_Its_Application_in_Radar_Target_Recognition
Shrinkage Estimator Generalizations of Proximal Support Vector Machines// Proceedings of the 8th ACM SIGKDD International Conference On Knowledge Discovery and Data Mining D Agarwal Support vector ...
https://www.sciencedirect.com/science/article/pii/S0957417415005679
Sparse Proximal Support Vector Machines is an embedded feature selection method. • sPSVMs removes more than 98% of features in many high dimensional datasets. • An efficient alternating optimization technique is proposed. • sPSVMs induces class-specific local sparsity.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2794982/
Diagonal Discriminant Analysis, Support Vector Machines and k-Nearest Neighbor have been suggested as among the best methods for small sample size situations, but none was found to be superior to others. In this article, we propose an improved diagonal discriminant approach through shrinkage and regularization of the variances.
https://www.sciencedirect.com/science/article/pii/S1875389212015568
Machine learning in automated text categorisation. Technical Report IEI-B4-31-1999, Istituto di Elaborazione dell’Informazione, 2001:22-53 [6] Agarwal, D.K.: Shrinkage estimator generalizations of proximal support vector machines,The 8th International Conference Knowledge Discovery and DataMining, pp.173–182.
https://www.arpm.co/lab/shrinkage-blending-assessing.html
Historically, the first shrinkage estimator was the James-Stein estimator of the expectation, refer to the original article [Stein, 1955] and see also [Lehmann and Casella, 1998]. To understand the James-Stein shrinkage estimator, let us start from the sample mean ˆ μ of a time series of invariants {ϵ 1, …, ϵ ˉ t} .
http://www.dia.fi.upm.es/uploads/asdm16/C11-SVMs-and-Regularized-Learning.pdf
• A Tutorial on Support Vector Machines for Pattern Recognition. C. J. C. Burges. Data Mining and Knowledge Discovery, Volume 2, 2002. • A Tutorial on Support Vector Regression. A. J. Smola and B. Schölkopf. Statistics and Computing, Volume 48, 2003. • Regression Shrinkage and Selection Via the Lasso. R. Tibshirani. Journal of the Royal ...
http://scialert.net/fulltext/?doi=itj.2014.2710.2719
Shrinkage estimator generalizations of proximal support vector machines. Proceedings of the 18th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, July 23-26, 2002, Edmonton, Alberta, Canada, pp: 173-182.
https://www.scientific.net/AMR.816-817.512
There has become a bottleneck to use support vector machine (SVM) due to the problems such as slow learning speed, large buffer memory requirement, low generalization performance and so on. These problems are caused by large-scale training sample set and outlier data immixed in the other class. Aiming at these problems, this paper proposed a new reduction strategy for large-scale training ...
https://www.scientific.net/AMM.421.701
There has become a bottleneck to use support vector machine (SVM) due to the problems such as slow learning speed, large buffer memory requirement, low generalization performance and so on. These problems are caused by large-scale training sample set and outlier data immixed in the other class. Aiming at these problems, this paper proposed a new reduction strategy for large-scale training ...
https://stats.stackexchange.com/questions/5727/james-stein-estimator-how-did-efron-and-morris-calculate-sigma2-in-shrinkag
Data analysis using Stein's estimator and its generalizations. R-1394-OEO, The RAND Corporation, March 1974 (link to pdf) . On page 312, you will see that Efron & Morris use an arc-sin transformation of these data, so that the variance of the batting averages is approximately unity:
https://www.kdnuggets.com/news/2002/n10/3i.html
Shrinkage Estimator Generalizations of Proximal Support Vector Machines Deepak Agarwal Interactive Deduplication using Active Learning Sunita Sarawagi,Anuradha Bhamidipaty Hierarchical Model-Based Clustering of Large Datasets Through Fractionation and Refractionation. Jeremy Tantrum, Werner Stuetzle, Alejandro Murua
http://papers.nips.cc/paper/4740-proximal-newton-type-methods-for-convex-optimization.pdf
support vector machines. This paper focuses on proximal Newton-type methods that were previously studied in [16, 18] and are closely related to the methods of Fukushima and Mine [10] and Tseng and Yun [21]. Both use search directions xthat are solutions to subproblems of …
http://www.ccsenet.org/journal/index.php/ijef/article/download/11815/8316
model, Nonlinear Support Vector Machine, High frequency Nikkei-225 data 1. Introduction Volatility, the standard deviation of the continuously compounded returns of a financial instrument over a specific time horizon, is both the boon and bane of all traders, you can’t live with it and you can’t really trade without it.
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4295837/
Network-Constrained Group Lasso for High-Dimensional Multinomial Classification with Application to Cancer Subtype Prediction Xinyu Tian , 1 Xuefeng Wang , 1, 2 and Jun Chen 3 1 Department of Applied Mathematics and Statistics, Stony Brook University, Stony Brook, NY, USA.
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