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» Learning Gaussian Process Models from Uncertain Data
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KDD
2006
ACM
118views Data Mining» more  KDD 2006»
14 years 7 months ago
Reducing the human overhead in text categorization
Many applications in text processing require significant human effort for either labeling large document collections (when learning statistical models) or extrapolating rules from...
Arnd Christian König, Eric Brill
NIPS
2004
13 years 8 months ago
Multiple Alignment of Continuous Time Series
Multiple realizations of continuous-valued time series from a stochastic process often contain systematic variations in rate and amplitude. To leverage the information contained i...
Jennifer Listgarten, Radford M. Neal, Sam T. Rowei...
UAI
2008
13 years 8 months ago
Feature Selection via Block-Regularized Regression
Identifying co-varying causal elements in very high dimensional feature space with internal structures, e.g., a space with as many as millions of linearly ordered features, as one...
Seyoung Kim, Eric P. Xing
SIGMETRICS
2010
ACM
143views Hardware» more  SIGMETRICS 2010»
14 years 6 days ago
Practical performance models for complex, popular applications
Perhaps surprisingly, no practical performance models exist for popular (and complex) client applications such as Adobe’s Creative Suite, Microsoft’s Office and Visual Studio...
Eno Thereska, Bjoern Doebel, Alice X. Zheng, Peter...
ECCV
2004
Springer
14 years 9 months ago
A Robust Algorithm for Characterizing Anisotropic Local Structures
This paper proposes a robust estimation and validation framework for characterizing local structures in a positive multi-variate continuous function approximated by a Gaussian-base...
Kazunori Okada, Dorin Comaniciu, Navneet Dalal, Ar...