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SIGKDD
2000
139views more  SIGKDD 2000»
15 years 3 months ago
Support Vector Machines: Hype or Hallelujah?
Support Vector Machines (SVMs) and related kernel methods have become increasingly popular tools for data mining tasks such as classification, regression, and novelty detection. T...
Kristin P. Bennett, Colin Campbell
131
Voted
SIGPRO
2010
111views more  SIGPRO 2010»
14 years 10 months ago
Semi-supervised speaker identification under covariate shift
In this paper, we propose a novel semi-supervised speaker identification method that can alleviate the influence of non-stationarity such as session dependent variation, the recor...
Makoto Yamada, Masashi Sugiyama, Tomoko Matsui
133
Voted
ICML
2007
IEEE
16 years 4 months ago
Transductive support vector machines for structured variables
We study the problem of learning kernel machines transductively for structured output variables. Transductive learning can be reduced to combinatorial optimization problems over a...
Alexander Zien, Ulf Brefeld, Tobias Scheffer
ICML
2008
IEEE
16 years 4 months ago
SVM optimization: inverse dependence on training set size
We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple ...
Shai Shalev-Shwartz, Nathan Srebro
131
Voted
VISUALIZATION
1998
IEEE
15 years 7 months ago
Eliminating popping artifacts in sheet buffer-based splatting
Splatting is a fast volume rendering algorithm which achieves its speed by projecting voxels in the form of pre-integrated interpolation kernels, or splats. Presently, two main va...
Klaus Mueller, Roger Crawfis