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217,110
Matrix-based Kernel Method for Large-scale Data Set
"... Abstract — In the computation process of many kernel methods, one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set. To overco ..."
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Abstract — In the computation process of many kernel methods, one of the important step is the formation of the kernel matrix. But the size of kernel matrix scales with the number of data set, it is infeasible to store and compute the kernel matrix when faced with the large-scale data set
An improved Generalized Discriminant Analysis for Large-scale data set
"... In order to overcome the computation and storage prob-lem for large-scale data set, an efficient iterative method of Generalized Discriminant Analysis is proposed. Because sample vectors cannot explicitly be denoted in kernel space, some mathematical tricks are firstly used to transform the kernel m ..."
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In order to overcome the computation and storage prob-lem for large-scale data set, an efficient iterative method of Generalized Discriminant Analysis is proposed. Because sample vectors cannot explicitly be denoted in kernel space, some mathematical tricks are firstly used to transform the kernel
Rendering for Visualizing Large- Scale Data Sets
"... For some time, researchers have done production visualization almost exclusively using high-end graphics workstations. They routinely archived and analyzed the outputs of simulations running on massively parallel supercomputers. Generally, a feature We describe two highly extraction step and a geome ..."
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geometric modeling step to significantly scalable, parallel software reduce the data’s size preceded the actual data rendering. Researchers volume-rendering also used this procedure to visualize large-scale data produced by algorithms—one renders high-resolution sensors and scanners. While the graphics work
Sequential Learning with LS-SVM for Large-Scale Data Sets
"... Abstract. We present a subspace-based variant of LS-SVMs (i.e. regularization networks) that sequentially processes the data and is hence especially suited for online learning tasks. The algorithm works by selecting from the data set a small subset of basis functions that is subsequently used to app ..."
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Cited by 2 (1 self)
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the approximation error incurred from approximating the kernel as well as the reduction of the cost in the original learning task. We use the large-scale data set ’forest ’ to compare performance and efficiency of our algorithm with greedy batch selection of the basis functions via orthogonal least squares. Using
An efficient Kernel Principal Component Analysis Algorithm for large-scale data set *
"... Abstract: Kernel principal component analysis (KPCA) is a popular nonlinear feature extraction method in the field of machine learning. It uses eigen-decomposition technique to extract the principal components. But the method is infeasible for large-scale data set because of the store and computatio ..."
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Abstract: Kernel principal component analysis (KPCA) is a popular nonlinear feature extraction method in the field of machine learning. It uses eigen-decomposition technique to extract the principal components. But the method is infeasible for large-scale data set because of the store
Visualisation of Large Scale Data Sets Contents Visualisation of Large Scale Data Sets
, 2005
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Scalable Computation of Distributions from Large Scale Data Sets
"... As we approach the era of exascale computing, the role of distributions to summarize, analyze and visualize large scale data is becoming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed framewor ..."
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As we approach the era of exascale computing, the role of distributions to summarize, analyze and visualize large scale data is becoming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed
Scalable Computation of Distributions from Large Scale Data Sets
"... As we approach the era of exascale computing, the role of distri-butions to summarize, analyze and visualize large scale data is be-coming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed framew ..."
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As we approach the era of exascale computing, the role of distri-butions to summarize, analyze and visualize large scale data is be-coming more and more important. Since histograms continue to be a popular way of modeling the underlying data distribution, we propose a scalable and distributed
Learning Concepts from Large-Scale Data Sets by Pairwise Coupling with Probabilistic Outputs
- IEEE Transactions on Neural Networks
, 1999
"... Abstract — This paper considers the problems of learning concepts from large-scale data sets. The way we take is completely classification algorithm independent. Firstly, the original problem is decomposed into a series of smaller two-class sub-problems which are easier to be solved. Secondly we pre ..."
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Cited by 1 (1 self)
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Abstract — This paper considers the problems of learning concepts from large-scale data sets. The way we take is completely classification algorithm independent. Firstly, the original problem is decomposed into a series of smaller two-class sub-problems which are easier to be solved. Secondly we
of mathematics content, students ’ learning of algebra, and analysis of large-scale data sets. Success after Failure: Academic Effects and Psychological Implications of Early Universal Algebra Policies
"... clude equity in mathematics access and instruction, technology integration into K–12 instruction, and statisti-cal analyses of large-scale data sets. MARTIN ROMERO is an assistant professor in the Department of Mathematics at Santa Ana College, ..."
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clude equity in mathematics access and instruction, technology integration into K–12 instruction, and statisti-cal analyses of large-scale data sets. MARTIN ROMERO is an assistant professor in the Department of Mathematics at Santa Ana College,
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