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NIPS
2003
15 years 1 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
AAAI
2006
15 years 1 months ago
Closest Pairs Data Selection for Support Vector Machines
This paper presents data selection procedures for support vector machines (SVM). The purpose of data selection is to reduce the dataset by eliminating as many non support vectors ...
Chaofan Sun
PAMI
2006
358views more  PAMI 2006»
15 years 12 days ago
Recovering 3D Human Pose from Monocular Images
We describe a learning based method for recovering 3D human body pose from single images and monocular image sequences. Our approach requires neither an explicit body model nor pri...
Ankur Agarwal, Bill Triggs
ICML
2004
IEEE
16 years 1 months ago
The Bayesian backfitting relevance vector machine
Traditional non-parametric statistical learning techniques are often computationally attractive, but lack the same generalization and model selection abilities as state-of-the-art...
Aaron D'Souza, Sethu Vijayakumar, Stefan Schaal
PRL
2006
114views more  PRL 2006»
15 years 12 days ago
Incremental training of support vector machines using hyperspheres
In the conventional incremental training of support vector machines, candidates for support vectors tend to be deleted if the separating hyperplane rotates as the training data ar...
Shinya Katagiri, Shigeo Abe