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https://papers.nips.cc/paper/2608-parallel-support-vector-machines-the-cascade-svm.pdf
Support Vector Machines are powerful classification and regression tools, but their compute and storage requirements increase rapidly with the number of training vectors, putting many problems of practical interest out of their reach. The core of an
https://www.datasciencecentral.com/profiles/blogs/machine-learning-in-parallel-with-support-vector-machines
Mar 19, 2014 · Support Vector Machines are a form of non-probabilistic binary linear classifiers which fit a “maximum-margin hyperplane in a transformed feature space”.
https://arxiv.org/pdf/1404.1066v1.pdf
plicitly parallel algorithm which is surprisingly efficient, permits a much simpler implementation, and leads to unprecedented speedups in SVM training. 1 Introduction Kernel support vector machines (SVM) are arguably among the most established machine learning algorithms.
https://journals.sagepub.com/doi/10.1260/1748-3018.4.2.231
Jun 01, 2010 · Support vector machines method with Gaussian kernel is applied to obtain the prediction model. For the first time, a parallel implementation of support vector machines is used to accelerate the model training process.Cited by: 66
https://www.statistik.tu-dortmund.de/~bischl/mypapers/support_vector_machines_on_large_data_sets_simple_parallel_approaches.pdf
Support Vector Machines on Large Data Sets: Simple Parallel Approaches 3 enabling us to tackle nonlinear problems with essentially linear techniques. The Gaussian kernel k(x i;x j) = exp ˙jjx i x jjj2 2 (2) is arguably the most important and popular kernel function and we have there-fore focused on it in all subsequent experiments. But note that all followingCited by: 9
https://www.researchgate.net/publication/261369013_Parallel_Support_Vector_Machines_in_Practice
Kernel support vector machines (SVM) are arguably among the most established machine learning algorithms. They can capture complex, nonlinear decision boundaries with good generalization to
https://arxiv.org/abs/1404.1066
In this paper, we evaluate the performance of various parallel optimization methods for Kernel Support Vector Machines on multicore CPUs and GPUs. In particular, we provide the first comparison of algorithms with explicit and implicit parallelization. Most existing parallel implementations for multi-core or GPU architectures are based on explicit parallelization of Sequential Minimal ...Cited by: 14
https://www.researchgate.net/publication/221619261_Parallel_Support_Vector_Machines_The_Cascade_SVM
Parallel Support Vector Machines: The Cascade SVM. ... Parallel implementations on a cluster of 16 processors were tested with over 1 million vectors (2-class problems), converging in a day or two ...
https://bibliographie.uni-tuebingen.de/xmlui/bitstream/handle/10900/49015/pdf/tech_21.pdf
Parallel Support Vector Machines Dominik Brugger Arbeitsbereich Technische Informatik Eberhard-Karls Universit¨at T ¨ubingen Sand 13, 72074 Tubingen¨ [email protected] Abstract The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained
https://www.sciencedirect.com/science/article/pii/S0950705119300450
In 2006, the generalized eigenvalue proximal support vector machine (GEPSVM) was proposed by Mangasarian and Wild , in which a pair of non-parallel hyperplanes are constructed by solving two generalized eigenvalue problems. Subsequently, a large number of non-parallel hyperplane classifiers were introduced.Cited by: 9
http://papers.nips.cc/paper/2608-parallel-support-vector-machines-the-cascade-svm.pdf
implemented on a single processor. Parallel implementations on a cluster of 16 processors were tested with over 1 million vectors (2-class problems), converging in a day or two, while a regular SVM never converged in over a week. 1 Introduction Support Vector Machines [1] are powerful classification and regression tools, but
https://www.datasciencecentral.com/profiles/blogs/machine-learning-in-parallel-with-support-vector-machines
Mar 19, 2014 · Machine Learning in Parallel with Support Vector Machines, Generalized Linear Models, and Adaptive Boosting. ... Three of these packages include Support Vector Machines (SVM) [1], Generalized Linear Models (GLM) [2], and Adaptive Boosting (AdaBoost) [3]. While all three packages can be highly accurate for various types of classification ...
https://journals.sagepub.com/doi/10.1260/1748-3018.4.2.231
Jun 01, 2010 · For the first time, a parallel implementation of support vector machines is used to accelerate the model training process. Our experimental results show very good performance of this approach, paving the way for further applications of support vector machines method on large energy consumption datasets.Cited by: 66
https://arxiv.org/pdf/1404.1066v1.pdf
plicitly parallel algorithm which is surprisingly efficient, permits a much simpler implementation, and leads to unprecedented speedups in SVM training. 1 Introduction Kernel support vector machines (SVM) are arguably among the most established machine learning algorithms.
https://www.semanticscholar.org/paper/Parallel-Support-Vector-Machines%3A-The-Cascade-SVM-Graf-Cosatto/61abf3eb0f653b67c0eb42c527b6620db51d4f3f
We describe an algorithm for support vector machines (SVM) that can be parallelized efficiently and scales to very large problems with hundreds of thousands of training vectors. Instead of analyzing the whole training set in one optimization step, the data are split into subsets and optimized separately with multiple SVMs. The partial results are combined and filtered again in a 'Cascade' of ...
http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.333.3078
CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): The Support Vector Machine (SVM) is a supervised algorithm for the solution of classification and regression problems. SVMs have gained widespread use in recent years because of successful applications like character recognition and the profound theoretical underpinnings concerning generalization performance.
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