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» Maximal Vector Computation in Large Data Sets
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ICS
2005
Tsinghua U.
14 years 2 months ago
The implications of working set analysis on supercomputing memory hierarchy design
Supercomputer architects strive to maximize the performance of scientific applications. Unfortunately, the large, unwieldy nature of most scientific applications has lead to the...
Richard C. Murphy, Arun Rodrigues, Peter M. Kogge,...
USS
2010
13 years 7 months ago
P4P: Practical Large-Scale Privacy-Preserving Distributed Computation Robust against Malicious Users
In this paper we introduce a framework for privacypreserving distributed computation that is practical for many real-world applications. The framework is called Peers for Privacy ...
Yitao Duan, NetEase Youdao, John Canny, Justin Z. ...
BMCBI
2006
211views more  BMCBI 2006»
13 years 9 months ago
Missing value estimation for DNA microarray gene expression data by Support Vector Regression imputation and orthogonal coding s
Background: Gene expression profiling has become a useful biological resource in recent years, and it plays an important role in a broad range of areas in biology. The raw gene ex...
Xian Wang, Ao Li, Zhaohui Jiang, Huanqing Feng
ECCV
2006
Springer
14 years 11 months ago
Dense Photometric Stereo by Expectation Maximization
Abstract. We formulate a robust method using Expectation Maximization (EM) to address the problem of dense photometric stereo. Previous approaches using Markov Random Fields (MRF) ...
Tai-Pang Wu, Chi-Keung Tang
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
14 years 22 days ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa