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SIGIR
2006
ACM
16 years 19 days ago
Large scale semi-supervised linear SVMs
Large scale learning is often realistic only in a semi-supervised setting where a small set of labeled examples is available together with a large collection of unlabeled data. In...
Vikas Sindhwani, S. Sathiya Keerthi
IUI
2004
ACM
16 years 3 days ago
Robust sketched symbol fragmentation using templates
Analysis of sketched digital ink is often aided by the division of stroke points into perceptually-salient fragments based on geometric features. Fragmentation has many applicatio...
Heloise Hwawen Hse, Michael Shilman, A. Richard Ne...
TOG
2008
174views more  TOG 2008»
15 years 6 months ago
A mass spring model for hair simulation
Our goal is to simulate the full hair geometry, consisting of approximately one hundred thousand hairs on a typical human head. This will require scalable methods that can simulat...
Andrew Selle, Michael Lentine, Ronald Fedkiw
CSDA
2007
136views more  CSDA 2007»
15 years 6 months ago
A note on iterative marginal optimization: a simple algorithm for maximum rank correlation estimation
The maximum rank correlation (MRC) estimator was originally studied by Han [1987. Nonparametric analysis of a generalized regression model. J. Econometrics 35, 303–316] and Sher...
Hansheng Wang
197
Voted
SADM
2008
178views more  SADM 2008»
15 years 6 months ago
Fast Projection-Based Methods for the Least Squares Nonnegative Matrix Approximation Problem
: Nonnegative matrix approximation (NNMA) is a popular matrix decomposition technique that has proven to be useful across a diverse variety of fields with applications ranging from...
Dongmin Kim, Suvrit Sra, Inderjit S. Dhillon