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» Learning from Highly Structured Data by Decomposition
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SIGMOD
2005
ACM
135views Database» more  SIGMOD 2005»
16 years 4 months ago
Mining data streams: a review
The recent advances in hardware and software have enabled the capture of different measurements of data in a wide range of fields. These measurements are generated continuously an...
Mohamed Medhat Gaber, Arkady B. Zaslavsky, Shonali...
TKDE
2008
195views more  TKDE 2008»
15 years 4 months ago
Learning a Maximum Margin Subspace for Image Retrieval
One of the fundamental problems in Content-Based Image Retrieval (CBIR) has been the gap between low-level visual features and high-level semantic concepts. To narrow down this gap...
Xiaofei He, Deng Cai, Jiawei Han
ESANN
2006
15 years 5 months ago
Margin based Active Learning for LVQ Networks
In this article, we extend a local prototype-based learning model by active learning, which gives the learner the capability to select training samples during the model adaptation...
Frank-Michael Schleif, Barbara Hammer, Thomas Vill...
SIGMOD
2002
ACM
219views Database» more  SIGMOD 2002»
16 years 4 months ago
Efficient k-NN search on vertically decomposed data
Applications like multimedia retrieval require efficient support for similarity search on large data collections. Yet, nearest neighbor search is a difficult problem in high dimen...
Arjen P. de Vries, Nikos Mamoulis, Niels Nes, Mart...
TNN
2008
178views more  TNN 2008»
15 years 4 months ago
IMORL: Incremental Multiple-Object Recognition and Localization
This paper proposes an incremental multiple-object recognition and localization (IMORL) method. The objective of IMORL is to adaptively learn multiple interesting objects in an ima...
Haibo He, Sheng Chen