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ICML
2005
IEEE
14 years 8 months ago
An efficient method for simplifying support vector machines
In this paper we describe a new method to reduce the complexity of support vector machines by reducing the number of necessary support vectors included in their solutions. The red...
DucDung Nguyen, Tu Bao Ho
CVPR
2009
IEEE
14 years 2 months ago
A distribution-based approach to tracking points in velocity vector fields
We address the problem of tracking points in dense vector fields. Such vector fields may come from computational fluid dynamics simulations, environmental monitoring sensors, o...
Liefei Xu, H. Quynh Dinh, Eugene Zhang, Zhongzang ...
ISMAR
2007
IEEE
14 years 2 months ago
Feature Tracking for Mobile Augmented Reality Using Video Coder Motion Vectors
We propose a novel, low-complexity, tracking scheme that uses motion vectors directly from a video coder. We compare our tracking algorithm against ground truth data, and show tha...
Gabriel Takacs, Vijay Chandrasekhar, Bernd Girod, ...
PAKDD
2007
ACM
128views Data Mining» more  PAKDD 2007»
14 years 1 months ago
Selecting a Reduced Set for Building Sparse Support Vector Regression in the Primal
Recent work shows that Support vector machines (SVMs) can be solved efficiently in the primal. This paper follows this line of research and shows how to build sparse support vector...
Liefeng Bo, Ling Wang, Licheng Jiao
NIPS
2003
13 years 9 months ago
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds
The decision functions constructed by support vector machines (SVM’s) usually depend only on a subset of the training set—the so-called support vectors. We derive asymptotical...
Ingo Steinwart