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ICML
2000
IEEE
16 years 4 months ago
FeatureBoost: A Meta-Learning Algorithm that Improves Model Robustness
Most machine learning algorithms are lazy: they extract from the training set the minimum information needed to predict its labels. Unfortunately, this often leads to models that ...
Joseph O'Sullivan, John Langford, Rich Caruana, Av...
EUROMICRO
2009
IEEE
15 years 7 months ago
Automated Feature Model-Based Generation of Refinement Transformations
Model-driven application engineering builds on the concept of model transformations. To weave additional refinement parts into an application model, so-called refinement transform...
Lucia Kapová, Thomas Goldschmidt
AVSS
2006
IEEE
15 years 10 months ago
A Random Field Model for Improved Feature Extraction and Tracking
This paper presents a novel method for illuminationinvariant and contrast preserving feature extraction, aimed at improving performance of tracking under complex light condition. ...
Xiaotong Yuan, Stan Z. Li
PR
2008
218views more  PR 2008»
15 years 3 months ago
Automatic feature localisation with constrained local models
We present an efficient and robust method of locating a set of feature points in an object of interest. From a training set we construct a joint model of the appearance of each fe...
David Cristinacce, Timothy F. Cootes
ENC
2004
IEEE
15 years 7 months ago
Feature Selection for Visual Gesture Recognition Using Hidden Markov Models
Hidden Markov models have become the preferred technique for visual recognition of human gestures. However, the recognition rate depends on the set of visual features used, and al...
José Antonio Montero, Luis Enrique Sucar