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» A supervised learning approach for imbalanced data sets
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ICASSP
2010
IEEE
15 years 4 months ago
Hierarchical dictionary learning for invariant classification
Sparse representation theory has been increasingly used in the fields of signal processing and machine learning. The standard sparse models are not invariant to spatial transform...
Leah Bar, Guillermo Sapiro
IROS
2008
IEEE
138views Robotics» more  IROS 2008»
15 years 10 months ago
Deep belief net learning in a long-range vision system for autonomous off-road driving
Abstract— We present a learning-based approach for longrange vision that is able to accurately classify complex terrain at distances up to the horizon, thus allowing high-level s...
Raia Hadsell, Ayse Erkan, Pierre Sermanet, Marco S...
KDD
2009
ACM
205views Data Mining» more  KDD 2009»
15 years 11 months ago
From active towards InterActive learning: using consideration information to improve labeling correctness
Data mining techniques have become central to many applications. Most of those applications rely on so called supervised learning algorithms, which learn from given examples in th...
Abraham Bernstein, Jiwen Li
DEXA
2009
Springer
151views Database» more  DEXA 2009»
15 years 10 months ago
Detecting Projected Outliers in High-Dimensional Data Streams
Abstract. In this paper, we study the problem of projected outlier detection in high dimensional data streams and propose a new technique, called Stream Projected Ouliter deTector ...
Ji Zhang, Qigang Gao, Hai H. Wang, Qing Liu, Kai X...
ECCV
2008
Springer
16 years 6 months ago
Scale Invariant Action Recognition Using Compound Features Mined from Dense Spatio-temporal Corners
Abstract. The use of sparse invariant features to recognise classes of actions or objects has become common in the literature. However, features are often "engineered" to...
Andrew Gilbert, John Illingworth, Richard Bowden