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» On Kernel Methods for Relational Learning
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CVPR
2003
IEEE
16 years 5 months ago
Object Class Recognition by Unsupervised Scale-Invariant Learning
We present a method to learn and recognize object class models from unlabeled and unsegmented cluttered scenes in a scale invariant manner. Objects are modeled as flexible constel...
Robert Fergus, Pietro Perona, Andrew Zisserman
IROS
2008
IEEE
125views Robotics» more  IROS 2008»
15 years 10 months ago
Neighborhood denoising for learning high-dimensional grasping manifolds
— Human control of high degree-of-freedom robotic systems, e.g. anthropomorphic robot hands, is often difficult due to the overwhelming number of variables that need to be speci...
Aggeliki Tsoli, Odest Chadwicke Jenkins
142
Voted
NLP
2000
15 years 7 months ago
Learning Rules for Large-Vocabulary Word Sense Disambiguation: A Comparison of Various Classifiers
In this article we compare the performance of various machine learning algorithms on the task of constructing word-sense disambiguation rules from data. The distinguishing characte...
Georgios Paliouras, Vangelis Karkaletsis, Ion Andr...
AUSAI
2008
Springer
15 years 5 months ago
Learning Object Representations Using Sequential Patterns
This paper explores the use of alternating sequential patterns of local features and saccading actions to learn robust and compact object representations. The temporal encoding rep...
Nobuyuki Morioka
93
Voted
PR
2008
91views more  PR 2008»
15 years 3 months ago
Applying the multi-category learning to multiple video object extraction
Video object (VO) extraction is of great importance in multimedia processing. In recent years approaches have been proposed to deal with VO extraction as a classification problem....
Yi Liu, Yuan F. Zheng, Xiaotong Shen