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» Semi-supervised Learning from General Unlabeled Data
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TNN
2008
178views more  TNN 2008»
13 years 7 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
ALT
2003
Springer
13 years 11 months ago
Efficient Learning of Ordered and Unordered Tree Patterns with Contractible Variables
Due to the rapid growth of tree structured data such as Web documents, efficient learning from tree structured data becomes more and more important. In order to represent structura...
Yusuke Suzuki, Takayoshi Shoudai, Satoshi Matsumot...
SIGMOD
2010
ACM
224views Database» more  SIGMOD 2010»
13 years 8 months ago
GDR: a system for guided data repair
Improving data quality is a time-consuming, labor-intensive and often domain specific operation. Existing data repair approaches are either fully automated or not efficient in int...
Mohamed Yakout, Ahmed K. Elmagarmid, Jennifer Nevi...
KDD
2010
ACM
222views Data Mining» more  KDD 2010»
13 years 10 months ago
Large linear classification when data cannot fit in memory
Recent advances in linear classification have shown that for applications such as document classification, the training can be extremely efficient. However, most of the existing t...
Hsiang-Fu Yu, Cho-Jui Hsieh, Kai-Wei Chang, Chih-J...
ICPR
2010
IEEE
13 years 6 months ago
Data Classification on Multiple Manifolds
Unlike most previous manifold-based data classification algorithms assume that all the data points are on a single manifold, we expect that data from different classes may reside ...
Rui Xiao, Qijun Zhao, David Zhang, Pengfei Shi