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PAMI
2011
14 years 13 days ago
Robust Object Tracking with Online Multiple Instance Learning
In this paper we address the problem of tracking an object in a video given its location in the first frame and no other information. Recently, a class of tracking techniques cal...
Boris Babenko, Ming-Hsuan Yang, Serge Belongie
ICRA
2008
IEEE
129views Robotics» more  ICRA 2008»
15 years 4 months ago
Orthogonal wall correction for visual motion estimation
— A good motion model is a prerequisite for many approaches to simultaneous localization and mapping. Without an absolute reference, it is however difficult to prevent drift whe...
Jörg Stückler, Sven Behnke
CORR
2004
Springer
122views Education» more  CORR 2004»
14 years 9 months ago
"In vivo" spam filtering: A challenge problem for data mining
Spam, also known as Unsolicited Commercial Email (UCE), is the bane of email communication. Many data mining researchers have addressed the problem of detecting spam, generally by...
Tom Fawcett
MLDM
2009
Springer
15 years 4 months ago
Drift-Aware Ensemble Regression
Abstract. Regression models are often required for controlling production processes by predicting parameter values. However, the implicit assumption of standard regression techniqu...
Frank Rosenthal, Peter Benjamin Volk, Martin Hahma...
ICDM
2003
IEEE
181views Data Mining» more  ICDM 2003»
15 years 2 months ago
Dynamic Weighted Majority: A New Ensemble Method for Tracking Concept Drift
Algorithms for tracking concept drift are important for many applications. We present a general method based on the Weighted Majority algorithm for using any online learner for co...
Jeremy Z. Kolter, Marcus A. Maloof