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AAAI
2010
15 years 3 months ago
Assisting Users with Clustering Tasks by Combining Metric Learning and Classification
Interactive clustering refers to situations in which a human labeler is willing to assist a learning algorithm in automatically clustering items. We present a related but somewhat...
Sumit Basu, Danyel Fisher, Steven M. Drucker, Hao ...
PAMI
2008
250views more  PAMI 2008»
15 years 1 months ago
Combined Top-Down/Bottom-Up Segmentation
We construct an image segmentation scheme that combines top-down (TD) with bottom-up (BU) processing. In the proposed scheme, segmentation and recognition are intertwined rather th...
Eran Borenstein, Shimon Ullman
JILP
2000
103views more  JILP 2000»
15 years 1 months ago
Comparing and Combining Profiles
How much do two profiles of the same program differ? When has a profile changed enough to warrant reexamination of the profiled program? And how should two or more profiles be com...
Serap A. Savari, Cliff Young
PERCOM
2010
ACM
15 years 3 days ago
All for one or one for all? Combining heterogeneous features for activity spotting
Abstract—Choosing the right feature for motion based activity spotting is not a trivial task. Often, features derived by intuition or that proved to work well in previous work ar...
Ulf Blanke, Bernt Schiele, Matthias Kreil, Paul Lu...
WS
2010
ACM
15 years 1 days ago
DLEJena: A practical forward-chaining OWL 2 RL reasoner combining Jena and Pellet
This paper describes DLEJena, a practical reasoner for the OWL 2 RL profile that combines the forward-chaining rule engine of Jena and the Pellet DL reasoner. This combination is...
Georgios Meditskos, Nick Bassiliades