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» Learning to Generate Fast Signal Processing Implementations
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ICASSP
2011
IEEE
12 years 11 months ago
Online Kernel SVM for real-time fMRI brain state prediction
The Support Vector Machine (SVM) methodology is an effective, supervised, machine learning method that gives stateof-the-art performance for brain state classification from funct...
Yongxin Taylor Xi, Hao Xu, Ray Lee, Peter J. Ramad...
VIS
2009
IEEE
399views Visualization» more  VIS 2009»
14 years 8 months ago
Visual Human+Machine Learning
In this paper we describe a novel method to integrate interactive visual analysis and machine learning to support the insight generation of the user. The suggested approach combine...
Raphael Fuchs, Jürgen Waser, Meister Eduard Gr...
ICDE
2004
IEEE
259views Database» more  ICDE 2004»
14 years 9 months ago
Querying about the Past, the Present, and the Future in Spatio-Temporal
Moving objects (e.g., vehicles in road networks) continuously generate large amounts of spatio-temporal information in the form of data streams. Efficient management of such strea...
Jimeng Sun, Dimitris Papadias, Yufei Tao, Bin Liu
JGTOOLS
2008
100views more  JGTOOLS 2008»
13 years 7 months ago
Proximity Cluster Trees
Hierarchical spatial data structures provide a means for organizing data for efficient processing. Most spatial data structures are optimized for performing queries, such as inters...
Elena Jakubiak Hutchinson, Sarah F. Frisken, Ronal...
TVLSI
2008
133views more  TVLSI 2008»
13 years 7 months ago
A Medium-Grain Reconfigurable Architecture for DSP: VLSI Design, Benchmark Mapping, and Performance
Reconfigurable hardware has become a well-accepted option for implementing digital signal processing (DSP). Traditional devices such as field-programmable gate arrays offer good fi...
Mitchell J. Myjak, José G. Delgado-Frias