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ICPR
2004
IEEE
14 years 10 months ago
Learning Spatial Context from Tracking using Penalised Likelihoods
MAP estimation of Gaussian mixtures through maximisation of penalised likelihoods was used to learn models of spatial context. This enabled prior beliefs about the scale, orientat...
Hammadi Nait-Charif, Stephen J. McKenna
ICVS
2009
Springer
14 years 3 months ago
Learning Query-Dependent Distance Metrics for Interactive Image Retrieval
An approach to target-based image retrieval is described based on on-line rank-based learning. User feedback obtained via interaction with 2D image layouts provides qualitative co...
Junwei Han, Stephen J. McKenna, Ruixuan Wang
ICPR
2010
IEEE
14 years 1 months ago
Gaussian Process Learning from Order Relationships Using Expectation Propagation
A method for Gaussian process learning of a scalar function from a set of pair-wise order relationships is presented. Expectation propagation is used to obtain an approximation to...
Ruixuan Wang, Stephen James Mckenna
ICPR
2010
IEEE
13 years 6 months ago
Cross Entropy Optimization of the Random Set Framework for Multiple Instance Learning
Abstract--Multiple instance learning (MIL) is a recently researched technique used for learning a target concept in the presence of noise. Previously, a random set framework for mu...
Jeremy Bolton, Paul D. Gader
CVPR
2008
IEEE
13 years 10 months ago
Improving local learning for object categorization by exploring the effects of ranking
Local learning for classification is useful in dealing with various vision problems. One key factor for such approaches to be effective is to find good neighbors for the learning ...
Tien-Lung Chang, Tyng-Luh Liu, Jen-Hui Chuang