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ICRA
2005
IEEE
176views Robotics» more  ICRA 2005»
15 years 8 months ago
Auto-supervised learning in the Bayesian Programming Framework
Domestic and real world robotics requires continuous learning of new skills and behaviors to interact with humans. Auto-supervised learning, a compromise between supervised and co...
Pierre Dangauthier, Pierre Bessière, Anne S...
ICMCS
2006
IEEE
97views Multimedia» more  ICMCS 2006»
15 years 9 months ago
Fast Progressive Model Refinement Global Motion Estimation Algorithm with Prediction
Global Motion Estimation (GME) is an important part in the object-based applications. In this paper, a fast progressive model refinement (FPMR) GME algorithm is proposed. It can s...
Haifeng Wang, Jia Wang, Qingshan Liu, Hanqing Lu
BMCBI
2010
132views more  BMCBI 2010»
15 years 3 months ago
Error margin analysis for feature gene extraction
Background: Feature gene extraction is a fundamental issue in microarray-based biomarker discovery. It is normally treated as an optimization problem of finding the best predictiv...
Chi Kin Chow, Hai Long Zhu, Jessica Lacy, Winston ...
GECCO
2008
Springer
206views Optimization» more  GECCO 2008»
15 years 3 months ago
Improving accuracy of immune-inspired malware detectors by using intelligent features
In this paper, we show that a Bio-inspired classifier’s accuracy can be dramatically improved if it operates on intelligent features. We propose a novel set of intelligent feat...
M. Zubair Shafiq, Syed Ali Khayam, Muddassar Faroo...
SAC
2006
ACM
15 years 8 months ago
The impact of sample reduction on PCA-based feature extraction for supervised learning
“The curse of dimensionality” is pertinent to many learning algorithms, and it denotes the drastic raise of computational complexity and classification error in high dimension...
Mykola Pechenizkiy, Seppo Puuronen, Alexey Tsymbal