Implementation of Support Vector Machines on the Cell Processor
High Performance Implementation of Support Vector Machines Using OpenCL
... This would require a rather sophisticated software stack, including a very intricate com- piler, and consequently might introduce a large overhead since OpenCL kernels are typi- cally compiled at run time and would be ...
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Support Vector Machines
... “off-the-shelf” supervised learning algorithm. To tell the SVM story, we’ll need to first talk about margins and the idea of separating data with a large “gap.” Next, we’ll talk about the optimal margin classifier, which ...
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Support Vector Machines
... E. Run Time Comparison Fig. 13 shows the training times for each algorithm. The results for the CS-SVM and OC-SVM are based on our Matlab implementation of solution path algorithms [8], [12] available at ...
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Support Vector Machines
... 5 Experiments We test and compare several ensemble learning algorithms, including our frame- work with the stump kernel, on various datasets. The first algorithm we test is our framework with the simplified stump kernel, ...
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Parallel Support Vector Machines
... This article described various ways how to parallelize SVM training for the original non- simplified SVM formulations including C-SVC ν-SVC, ε-SVR and ν-SVR. Three differ- ent parallelization strategies arise from the ...
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Support Vector Machines in R
... of support vector machines to train the overall CPU time used is less compared to the one-against-all method since the problems are smaller and the SVM optimization problem scales ...improved ...
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Survival support vector machines
... our implementation with the Cox PH model, RSF and gradient ...an implementation of a variant of the ranking approach (Evers and Messow, 2008) in the ...
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Scalable Multilevel Support Vector Machines
... 3 Computational Results Discussion and full results of our work can be found in [5]. The multilevel (W)SVM are evalu- ated on binary classification benchmarks from UCI repository. Single SVM and WSVM models are solved ...
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Support Vector Machines with a Reject Option
... We introduce a piecewise linear and convex training criterion dedicated to the problem of classi- fication with the reject option. Our proposal, inspired by the probabilistic interpretation of SVM fitting (Grandvalet et ...
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Multiclass proximal support vector machines
... The implementation of the SVM or MSVM demands solving a quadratic programming under linear ...Proximal support vector machines (PSVM) was introduced recently as a variant of SVM for binary ...
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Application of support vector machines for T-cell epitopes prediction
... MHC binders are not always T-cell epitopes however. Efforts to predict candidate T-cell epitopes have been util- ized ANNs (Honeyman et al., 1998). A full ANN with an indicator for each amino acid at each ...
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Binarized Support Vector Machines
... 4.6. Presence of Outliers The classifier proposed in this paper is based on threshold functions; thus, it seems that extreme obser- vations, with very high or very low values, will not have a strong influence in the ...
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Binarized support vector machines
... 4.6 Presence of outliers The classifier proposed in this paper is based on threshold functions, thus it seems that extreme observations, with very high or very low values, will not have a strong influence in the ...
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Lagrangian Support Vector Machines
... Xeon processor and a maximum of 2 Gigabytes of memory available for each ...multiprocessor machines. However, only one processor was used for all the experiments shown here as MATLAB is a single ...
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Binarized support vector machines
... 4.6. Presence of Outliers The classifier proposed in this paper is based on threshold functions; thus, it seems that extreme obser- vations, with very high or very low values, will not have a strong influence in the ...
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Support Vector Machines
... Defining the margin as the distance from the hyperplane to the nearest example, the basic observation is that intuitively, we expect a hyperplane with larger margin to generalize better [r] ...
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Support Vector Machines
... Geometrically, the vector w is directed orthogonal to the line defined by w T x = b. This can be understood as follows. First take b = 0. Now it is clear that all vectors, x, with vanishing inner product with w ...
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Chunking with Support Vector Machines
... 5 Summary In this paper, we introduce a uniform framework for chunking task based on Support Vector Machines (SVMs). Experimental results on WSJ corpus show that our method outperforms other ...
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Support Vector Machines Explained
... the Support Vector Machines (SVM), initially conceived of by Cortes and Vapnik [1], as sim- ple to understand as possible for those with minimal experience of Machine ...culus, vector geometry ...
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Support Vector Machines for Regression
... •The decision function is fully specified by a subset of the training data, the support vectors.. Introduction Introduction.[r] ...
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