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NIPS
1998
13 years 9 months ago
Dynamically Adapting Kernels in Support Vector Machines
The kernel-parameter is one of the few tunable parameters in Support Vector machines, controlling the complexity of the resulting hypothesis. Its choice amounts to model selection...
Nello Cristianini, Colin Campbell, John Shawe-Tayl...
ECCV
2004
Springer
14 years 9 months ago
Adaptive Probabilistic Visual Tracking with Incremental Subspace Update
Visual tracking, in essence, deals with non-stationary data streams that change over time. While most existing algorithms are able to track objects well in controlled environments,...
David A. Ross, Jongwoo Lim, Ming-Hsuan Yang
ICCV
2011
IEEE
12 years 7 months ago
Incremental On-line Semi-supervised Learning for Segmenting the Left Ventricle of the Heart from Ultrasound Data
Recently, there has been an increasing interest in the investigation of statistical pattern recognition models for the fully automatic segmentation of the left ventricle (LV) of t...
Gustavo Carneiro, Jacinto C. Nascimento
AGENTS
2001
Springer
14 years 4 days ago
Using rat navigation models to learn orientation from visual input on a mobile robot
Rodents possess extraordinary navigation abilities that are far in excess of what current state-of-the-art robot agents are capable of. This paper describes research that is part ...
Brett Browning
ICPR
2006
IEEE
14 years 8 months ago
Learning Mixtures of Offline and Online features for Handwritten Stroke Recognition
In this paper we propose a novel scheme to combine offline and online features of handwritten strokes. The stateof-the-art methods in handwritten stroke recognition have used a pr...
C. V. Jawahar, Karteek Alahari, Satya Lahari Putre...