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CVPR
2008
IEEE
15 years 11 months ago
Parameterized Kernel Principal Component Analysis: Theory and applications to supervised and unsupervised image alignment
Parameterized Appearance Models (PAMs) (e.g. eigentracking, active appearance models, morphable models) use Principal Component Analysis (PCA) to model the shape and appearance of...
Fernando De la Torre, Minh Hoai Nguyen
ICML
2006
IEEE
15 years 10 months ago
Deterministic annealing for semi-supervised kernel machines
An intuitive approach to utilizing unlabeled data in kernel-based classification algorithms is to simply treat unknown labels as additional optimization variables. For marginbased...
Vikas Sindhwani, S. Sathiya Keerthi, Olivier Chape...
GECCO
2007
Springer
187views Optimization» more  GECCO 2007»
15 years 3 months ago
Defining implicit objective functions for design problems
In many design tasks it is difficult to explicitly define an objective function. This paper uses machine learning to derive an objective in a feature space based on selected examp...
Sean Hanna
SIGIR
2003
ACM
15 years 3 months ago
Question classification using support vector machines
Question classification is very important for question answering. This paper presents our research work on automatic question classification through machine learning approaches. W...
Dell Zhang, Wee Sun Lee
ECML
2006
Springer
15 years 1 months ago
The Minimum Volume Covering Ellipsoid Estimation in Kernel-Defined Feature Spaces
Minimum volume covering ellipsoid estimation is important in areas such as systems identification, control, video tracking, sensor management, and novelty detection. It is well kno...
Alexander N. Dolia, Tijl De Bie, Christopher J. Ha...