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AROBOTS
2010
128views more  AROBOTS 2010»
15 years 6 months ago
Track-based self-supervised classification of dynamic obstacles
Abstract This work introduces a self-supervised architecture for robust classification of moving obstacles in urban environments. Our approach presents a hierarchical scheme that r...
Roman Katz, Juan Nieto, Eduardo Mario Nebot, Bertr...
JMLR
2008
133views more  JMLR 2008»
15 years 6 months ago
Algorithms for Sparse Linear Classifiers in the Massive Data Setting
Classifiers favoring sparse solutions, such as support vector machines, relevance vector machines, LASSO-regression based classifiers, etc., provide competitive methods for classi...
Suhrid Balakrishnan, David Madigan
JAIR
2006
102views more  JAIR 2006»
15 years 6 months ago
Multiple-Goal Heuristic Search
This paper presents a new framework for anytime heuristic search where the task is to achieve as many goals as possible within the allocated resources. We show the inadequacy of t...
Dmitry Davidov, Shaul Markovitch
TNN
2008
119views more  TNN 2008»
15 years 6 months ago
Selecting Useful Groups of Features in a Connectionist Framework
Abstract--Suppose for a given classification or function approximation (FA) problem data are collected using sensors. From the output of the th sensor, features are extracted, ther...
Debrup Chakraborty, Nikhil R. Pal
JIRS
2007
78views more  JIRS 2007»
15 years 6 months ago
Online-learning and Attention-based Approach to Obstacle Avoidance Using a Range Finder
The problem of developing local reactive obstacle-avoidance behaviors by a mobile robot through online real-time learning is considered. The robot operated in an unknown bounded 2...
Shuqing Zeng, Juyang Weng