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» Maximal Vector Computation in Large Data Sets
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HPDC
2000
IEEE
14 years 1 months ago
dQUOB: Managing Large Data Flows using Dynamic Embedded Queries
The dQUOB system satis es client need for speci c information from high-volume data streams. The data streams we speak of are the ow of data existing during large-scale visualizat...
Beth Plale, Karsten Schwan
NIPS
2004
13 years 10 months ago
Parallel Support Vector Machines: The Cascade SVM
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. I...
Hans Peter Graf, Eric Cosatto, Léon Bottou,...
CVPR
2008
IEEE
14 years 11 months ago
Semi-Supervised Discriminant Analysis using robust path-based similarity
Linear Discriminant Analysis (LDA), which works by maximizing the within-class similarity and minimizing the between-class similarity simultaneously, is a popular dimensionality r...
Yu Zhang, Dit-Yan Yeung
CIKM
2004
Springer
14 years 2 months ago
Framework and algorithms for trend analysis in massive temporal data sets
Mining massive temporal data streams for significant trends, emerging buzz, and unusually high or low activity is an important problem with several commercial applications. In th...
Sreenivas Gollapudi, D. Sivakumar
HIPC
2005
Springer
14 years 2 months ago
XCAT-C++: Design and Performance of a Distributed CCA Framework
In this paper we describe the design and implementation of a C++ based Common Component Architecture (CCA) framework, XCAT-C++. It can efficiently marshal and unmarshal large data...
Madhusudhan Govindaraju, Michael R. Head, Kenneth ...