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ICML
2006
IEEE
16 years 4 months ago
Bayesian regression with input noise for high dimensional data
This paper examines high dimensional regression with noise-contaminated input and output data. Goals of such learning problems include optimal prediction with noiseless query poin...
Jo-Anne Ting, Aaron D'Souza, Stefan Schaal
JASIS
2006
96views more  JASIS 2006»
15 years 3 months ago
Learning to classify documents according to genre
Genre or style analysis can be used to improve results achieved using standard IR techniques. A genre class is a group of documents that are written in a similar style. Genre clas...
Aidan Finn, Nicholas Kushmerick
JMLR
2002
115views more  JMLR 2002»
15 years 2 months ago
PAC-Bayesian Generalisation Error Bounds for Gaussian Process Classification
Approximate Bayesian Gaussian process (GP) classification techniques are powerful nonparametric learning methods, similar in appearance and performance to support vector machines....
Matthias Seeger
ICML
2008
IEEE
16 years 4 months ago
Adaptive p-posterior mixture-model kernels for multiple instance learning
In multiple instance learning (MIL), how the instances determine the bag-labels is an essential issue, both algorithmically and intrinsically. In this paper, we show that the mech...
Hua-Yan Wang, Qiang Yang, Hongbin Zha
ICCV
2007
IEEE
16 years 5 months ago
Graph Based Discriminative Learning for Robust and Efficient Object Tracking
Object tracking is viewed as a two-class 'one-versusrest' classification problem, in which the sample distribution of the target is approximately Gaussian while the back...
Xiaoqin Zhang, Weiming Hu, Stephen J. Maybank, Xi ...