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COLT
2006
Springer
14 years 2 months ago
Discriminative Learning Can Succeed Where Generative Learning Fails
Generative algorithms for learning classifiers use training data to separately estimate a probability model for each class. New items are classified by comparing their probabiliti...
Philip M. Long, Rocco A. Servedio
ICCV
2009
IEEE
13 years 8 months ago
Learning image similarity from Flickr groups using Stochastic Intersection Kernel MAchines
Measuring image similarity is a central topic in computer vision. In this paper, we learn similarity from Flickr groups and use it to organize photos. Two images are similar if th...
Gang Wang, Derek Hoiem, David A. Forsyth
ICML
2003
IEEE
14 years 11 months ago
Discriminative Gaussian Mixture Models: A Comparison with Kernel Classifiers
We show that a classifier based on Gaussian mixture models (GMM) can be trained discriminatively to improve accuracy. We describe a training procedure based on the extended Baum-W...
Aldebaro Klautau, Nikola Jevtic, Alon Orlitsky
CVPR
2007
IEEE
15 years 29 days ago
Online Learning Asymmetric Boosted Classifiers for Object Detection
We present an integrated framework for learning asymmetric boosted classifiers and online learning to address the problem of online learning asymmetric boosted classifiers, which ...
Minh-Tri Pham, Tat-Jen Cham
ICPR
2010
IEEE
14 years 3 months ago
Evolving Fuzzy Classifiers: Application to Incremental Learning of Handwritten Gesture Recognition Systems
In this paper, we present a new method to design customizable self-evolving fuzzy rule-based classifiers. The presented approach combines an incremental clustering algorithm with a...
Abdullah Almaksour, Eric Anquetil, Solen Quiniou, ...