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» Learning spatial relations in object recognition
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ICASSP
2011
IEEE
12 years 11 months ago
Application specific loss minimization using gradient boosting
Gradient boosting is a flexible machine learning technique that produces accurate predictions by combining many weak learners. In this work, we investigate its use in two applica...
Bin Zhang, Abhinav Sethy, Tara N. Sainath, Bhuvana...
SG
2007
Springer
14 years 1 months ago
Correlating Text and Images: Concept and Evaluation
This paper presents the concept and an evaluation of a novel approach to support students to understand complex spatial relations and to learn unknown terms of a domain-specific t...
Timo Götzelmann, Pere-Pau Vázquez, Knu...
AR
2006
95views more  AR 2006»
13 years 7 months ago
Adaptive body schema for robotic tool-use
The development and expression of many higher level cognitive functions, such as imitation, spatial perception, and tool-use relies on a multi-modal representation of the body kno...
Cota Nabeshima, Yasuo Kuniyoshi, Max Lungarella
MM
2003
ACM
241views Multimedia» more  MM 2003»
14 years 27 days ago
Invariance in motion analysis of videos
In this paper, we propose an approach that retrieves motion of objects from the videos based on the dynamic time warping of view invariant characteristics. The motion is represent...
Cen Rao, Mubarak Shah, Tanveer Fathima Syeda-Mahmo...
SDM
2012
SIAM
252views Data Mining» more  SDM 2012»
11 years 10 months ago
Learning from Heterogeneous Sources via Gradient Boosting Consensus
Multiple data sources containing different types of features may be available for a given task. For instance, users’ profiles can be used to build recommendation systems. In a...
Xiaoxiao Shi, Jean-François Paiement, David...