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» Learning from Ambiguously Labeled Examples
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CIKM
2009
Springer
13 years 10 months ago
Heterogeneous cross domain ranking in latent space
Traditional ranking mainly focuses on one type of data source, and effective modeling still relies on a sufficiently large number of labeled or supervised examples. However, in m...
Bo Wang, Jie Tang, Wei Fan, Songcan Chen, Zi Yang,...
BMCBI
2006
158views more  BMCBI 2006»
13 years 5 months ago
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
SIGMOD
2011
ACM
242views Database» more  SIGMOD 2011»
12 years 8 months ago
The SystemT IDE: an integrated development environment for information extraction rules
Information Extraction (IE) — the problem of extracting structured information from unstructured text — has become the key enabler for many enterprise applications such as sem...
Laura Chiticariu, Vivian Chu, Sajib Dasgupta, Thil...
WWW
2005
ACM
14 years 6 months ago
Web data extraction based on partial tree alignment
This paper studies the problem of extracting data from a Web page that contains several structured data records. The objective is to segment these data records, extract data items...
Yanhong Zhai, Bing Liu
KDD
2009
ACM
230views Data Mining» more  KDD 2009»
14 years 6 months ago
Cross domain distribution adaptation via kernel mapping
When labeled examples are limited and difficult to obtain, transfer learning employs knowledge from a source domain to improve learning accuracy in the target domain. However, the...
ErHeng Zhong, Wei Fan, Jing Peng, Kun Zhang, Jiang...