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ICCV
2007
IEEE
15 years 9 months ago
Co-Tracking Using Semi-Supervised Support Vector Machines
This paper treats tracking as a foreground/background classification problem and proposes an online semisupervised learning framework. Initialized with a small number of labeled ...
Feng Tang, Shane Brennan, Qi Zhao, Hai Tao
KCAP
2009
ACM
15 years 9 months ago
Interactively shaping agents via human reinforcement: the TAMER framework
As computational learning agents move into domains that incur real costs (e.g., autonomous driving or financial investment), it will be necessary to learn good policies without n...
W. Bradley Knox, Peter Stone
CVPR
2008
IEEE
16 years 4 months ago
Decomposition, discovery and detection of visual categories using topic models
We present a novel method for the discovery and detection of visual object categories based on decompositions using topic models. The approach is capable of learning a compact and...
Mario Fritz, Bernt Schiele
ICDE
2008
IEEE
141views Database» more  ICDE 2008»
16 years 4 months ago
SPOT: A System for Detecting Projected Outliers From High-dimensional Data Streams
In this paper, we present a new technique, called Stream Projected Ouliter deTector (SPOT), to deal with outlier detection problem in high-dimensional data streams. SPOT is unique ...
Ji Zhang, Qigang Gao, Hai H. Wang
KDD
2004
ACM
132views Data Mining» more  KDD 2004»
16 years 3 months ago
A probabilistic framework for semi-supervised clustering
Unsupervised clustering can be significantly improved using supervision in the form of pairwise constraints, i.e., pairs of instances labeled as belonging to same or different clu...
Sugato Basu, Mikhail Bilenko, Raymond J. Mooney