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https://arxiv.org/pdf/1211.6085.pdf
Random Projections for Linear Support Vector Machines :3 1.2. Dimension Reduction Our goal is to study how the SVM performs under (linear) dimensionality reduction transformations in the feature space. Let R ∈ Rd×r be the dimension reduction matrix that reduces the dimensionality of the input from d to r ≪ d.
https://www.researchgate.net/publication/268811967_Random_Projections_for_Support_Vector_Machines
The linear support vector machine constructs a hyperplane... Find, read and cite all the research you need on ResearchGate ... Random Projections for Support Vector Machines. ... that random ...
http://proceedings.mlr.press/v31/paul13a.pdf
499 Random Projections for Support Vector Machines vectors. The spectral norm of A is kAk 2 = ˙ 1. We introduce matrix notation that we will use for the remainder of the paper.Cited by: 46
http://www.cs.rpi.edu/~pauls2/Paul_TKDD14.pdf
Random Projections for Linear Support Vector Machines 22:3 singular values σ 1 ≥ σ 2 ≥ ···σρ > 0, and V ∈ Rd×ρ is a matrix containing the right singular vectors. The spectral norm of A is A 2 = σ 1. 1.1.1. SVM Classification.Let X ∈ Rn×d be the matrix whose rows are the vectors xT i,
https://arxiv.org/abs/1211.6085
Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum enclosing ball in ...Cited by: 2
http://proceedings.mlr.press/v31/paul13a.html
Apr 29, 2013 · %0 Conference Paper %T Random Projections for Support Vector Machines %A Saurabh Paul %A Christos Boutsidis %A Malik Magdon-Ismail %A Petros Drineas %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep Ravikumar %F ...Cited by: 46
https://archive.org/stream/arxiv-1211.6085/1211.6085_djvu.txt
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http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.3724
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Let X be a data matrix of rank ρ, representing n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X.
http://alumni.cs.ucr.edu/~mvlachos/pubs/svm1D.pdf
Solving Linear SVMs with Multiple 1D Projections ... linear Support Vector Machines (SVMs) that capitalizes on multiple 1D projections. We show that the approach approximates the optimal solution with high accuracy and comes with ana-lytical guarantees. Our solution adapts on methodologies from random projections, exponential search, and coordi
https://ui.adsabs.harvard.edu/abs/2012arXiv1211.6085P/abstract
Nov 01, 2012 · Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and minimum …Cited by: 28
http://proceedings.mlr.press/v31/paul13a.pdf
499 Random Projections for Support Vector Machines vectors. The spectral norm of A is kAk 2 = ˙ 1. We introduce matrix notation that we will use for the remainder of the paper.Cited by: 46
https://arxiv.org/pdf/1211.6085.pdf
Random Projections for Linear Support Vector Machines :3 1.2. Dimension Reduction Our goal is to study how the SVM performs under (linear) dimensionality reduction transformations in the feature space. Let R ∈ Rd×r be the dimension reduction matrix that reduces the dimensionality of the input from d to r ≪ d.
https://www.researchgate.net/publication/268811967_Random_Projections_for_Support_Vector_Machines
The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new obliv-ious dimension reduction technique which is precomputed and can be ...
https://deepai.org/publication/random-projections-for-linear-support-vector-machines
Nov 26, 2012 · Support Vector Machines (SVM) [Cristianini and Shawe-Taylor (2000)] are extremely popular in machine learning today. They have been used in both classification and regression. For classification, the training data set consists of
https://arxiv.org/abs/1211.6085
Let X be a data matrix of rank ρ, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and …Cited by: 2
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.380.3724
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Let X be a data matrix of rank ρ, representing n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any …
https://www.researchgate.net/publication/233763973_Random_Projections_for_Linear_Support_Vector_Machines
The linear support vector machine constructs a... Find, read and cite all the research you need on ResearchGate ... Random Projections for Linear Support Vector Machines…
http://www.cs.rpi.edu/~pauls2/Paul_TKDD14.pdf
Random Projections for Linear Support Vector Machines 22:3 singular values σ 1 ≥ σ 2 ≥ ···σρ > 0, and V ∈ Rd×ρ is a matrix containing the right singular vectors. The spectral norm of A is A 2 = σ 1. 1.1.1. SVM Classification.Let X ∈ Rn×d be the matrix whose rows are the vectors xT i, Y ∈ R n× be the diagonal matrix with entries Y ii = y i,andα = [α 1,α 2,...,α
https://ui.adsabs.harvard.edu/abs/2012arXiv1211.6085P/abstract
Nov 01, 2012 · Random Projections for Linear Support Vector Machines - NASA/ADS Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin.Cited by: 28
http://proceedings.mlr.press/v31/paul13a.html
Apr 29, 2013 · %0 Conference Paper %T Random Projections for Support Vector Machines %A Saurabh Paul %A Christos Boutsidis %A Malik Magdon-Ismail %A Petros Drineas %B Proceedings of the Sixteenth International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2013 %E Carlos M. Carvalho %E Pradeep …Cited by: 46
https://ui.adsabs.harvard.edu/abs/2012arXiv1211.6085P/abstract
Let X be a data matrix of rank \rho, whose rows represent n points in d-dimensional space. The linear support vector machine constructs a hyperplane separator that maximizes the 1-norm soft margin. We develop a new oblivious dimension reduction technique which is precomputed and can be applied to any input matrix X. We prove that, with high probability, the margin and …
https://nuit-blanche.blogspot.com/2012/12/random-projections-for-support-vector.html
Dec 21, 2012 · A few references have been featured here under the RandNLA tag Check also the Randomized Numerical Linear Algebra page.The thesis of Saurabh Paul entitled Random Projections for Support Vector Machines is here.
https://jakevdp.github.io/PythonDataScienceHandbook/05.07-support-vector-machines.html
Support vector machines (SVMs) are a particularly powerful and flexible class of supervised algorithms for both classification and regression. In this section, we will develop the intuition behind support vector machines and their use in classification problems. We begin with the standard imports:
https://asu.pure.elsevier.com/en/publications/drsvm-distributed-random-projection-algorithms-for-svms
We present distributed random projected gradient algorithms for Support Vector Machines (SVMs) that can be used by multiple agents connected over a time-varying network. The goal is for the agents to cooperatively find the same maximum margin hyperplane.
http://alumni.cs.ucr.edu/~mvlachos/pubs/svm1D.pdf
A random projection L is a random vector originating from the origin and going to a randomly chosen point in the d-dimensional unit sphere. Random projections have …
https://people.eecs.berkeley.edu/~brecht/papers/07.rah.rec.nips.pdf
The most popular methods for large-scale kernel machines are decomposition methods for solving Support Vector Machines (SVM). These methods iteratively update a subset of the kernel machine’s coefficients using coordinate ascent until KKT conditions are satisfied to within a tolerance [5, 6].
https://ieeexplore.ieee.org/document/5413651/
In this paper, we are exploring the emerging method of Random Projections (RP), a data independent linear projection method, for dimensionality reduction in the context of face detection. ... that is comparable to that obtained with traditional dimensionality reduction techniques for face detection using support vector machines.
https://www.sciencedirect.com/science/article/abs/pii/S0031320319302882
Support Vector Machines are designed to withstand noise in data. ... The proposed approach introduces a layer of uncertainty through the use of random projections on top of the learners, making it challenging for the adversary to guess the specific configurations of the learners. To find appropriate projection directions, we introduce novel ...
https://blog.statsbot.co/support-vector-machines-tutorial-c1618e635e93
Aug 15, 2017 · If you have used machine learning to perform classification, you might have heard about Support Vector Machines (SVM).Introduced a little more than 50 years ago, they have evolved over time and have also been adapted to various other problems like regression, outlier analysis, and ranking.. SVMs are a favorite tool in the arsenal of many machine learning …
https://archive.org/stream/arxiv-1211.6085/1211.6085_djvu.txt
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https://link.springer.com/article/10.1007/s10994-019-05865-4
Feature selection using support vector machines. In Proceedings of the international conference on data mining methods and databases for engineering, finance, and other fields (pp. 84–89). Google Scholar
https://papers.ssrn.com/sol3/papers.cfm?abstract_id=876544
Jan 24, 2006 · Random forest and Support Vector Machines (SVM) are very specific type of machine learning method, and are promising tools for the prediction of financial time series. The tested classification models, which predict direction, include linear discriminant analysis, logit, artificial neural network, random forest and SVM.
https://www.youtube.com/watch?v=eHsErlPJWUU
May 18, 2012 · Support Vector Machines - One of the most successful learning algorithms; getting a complex model at the price of a simple one. Lecture 14 of 18 of Caltech's Machine Learning Course - CS 156 by ...
http://statistics.rutgers.edu/home/pingli/papers/RPCode.pdf
Coding for Random Projections Ping Li Department of Statistics & Biostatistics Department of Computer Science Rutgers University Piscataway, NJ 08854 [email protected] Michael
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