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» A Framework for Multiple-Instance Learning
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195
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PKDD
2009
Springer
152views Data Mining» more  PKDD 2009»
16 years 1 months ago
Feature Selection for Value Function Approximation Using Bayesian Model Selection
Abstract. Feature selection in reinforcement learning (RL), i.e. choosing basis functions such that useful approximations of the unkown value function can be obtained, is one of th...
Tobias Jung, Peter Stone
ICASSP
2008
IEEE
16 years 1 months ago
Contextually adaptive signal representation using conditional principal component analysis
The conventional method of generating a basis that is optimally adapted (in MSE) for representation of an ensemble of signals is Principal Component Analysis (PCA). A more ambitio...
Rosa M. Figueras i Ventura, Umesh Rajashekar, Zhou...
193
Voted
ICDM
2008
IEEE
190views Data Mining» more  ICDM 2008»
16 years 1 months ago
Simultaneous Co-segmentation and Predictive Modeling for Large, Temporal Marketing Data
Several marketing problems involve prediction of customer purchase behavior and forecasting future preferences. We consider predictive modeling of large scale, bi-modal or multimo...
Meghana Deodhar, Joydeep Ghosh
IROS
2008
IEEE
118views Robotics» more  IROS 2008»
16 years 1 months ago
Laban Movement Analysis for multi-ocular systems
Abstract— We present as a contribution to the field of humanmachine interaction a system that analyzes human movements online through multiple observers, based on the concept of...
Jörg Rett, Luis Santos, Jorge Dias
196
Voted
CVPR
2007
IEEE
16 years 1 months ago
Trajectory Series Analysis based Event Rule Induction for Visual Surveillance
In this paper, a generic rule induction framework based on trajectory series analysis is proposed to learn the event rules. First the trajectories acquired by a tracking system ar...
Zhang Zhang, Kaiqi Huang, Tieniu Tan, Liangsheng W...