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» Learning parallel portfolios of algorithms
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CVPR
2005
IEEE
16 years 5 months ago
Learning to Estimate Human Pose with Data Driven Belief Propagation
We propose a statistical formulation for 2-D human pose estimation from single images. The human body configuration is modeled by a Markov network and the estimation problem is to...
Gang Hua, Ming-Hsuan Yang, Ying Wu
145
Voted
IFIP12
2008
15 years 4 months ago
P-Prism: A Computationally Efficient Approach to Scaling up Classification Rule Induction
Top Down Induction of Decision Trees (TDIDT) is the most commonly used method of constructing a model from a dataset in the form of classification rules to classify previously unse...
Frederic T. Stahl, Max A. Bramer, Mo Adda
IPPS
2003
IEEE
15 years 8 months ago
SPMD Image Processing on Beowulf Clusters: Directives and Libraries
Most image processing algorithms can be parallelized by splitting parallel loops and by using very few communication patterns. Code parallelization using MPI still involves much p...
Paulo F. Oliveira, J. M. Hans du Buf
128
Voted
SC
2000
ACM
15 years 7 months ago
Scalable Algorithms for Adaptive Statistical Designs
We present a scalable, high-performance solution to multidimensional recurrences that arise in adaptive statistical designs. Adaptive designs are an important class of learning al...
Robert H. Oehmke, Janis Hardwick, Quentin F. Stout
121
Voted
ICPADS
2005
IEEE
15 years 9 months ago
Distributed Spanning Tree Algorithms for Large Scale Traversals
— The Distributed Spanning Tree (DST) is an overlay structure designed to be scalable. It supports the growth from small scale to large scale. The DST is a tree without bottlenec...
Sylvain Dahan