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» Learning Classifiers from Semantically Heterogeneous Data
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ICMLA
2010
13 years 6 months ago
Smoothing Gene Expression Using Biological Networks
Gene expression (microarray) data have been used widely in bioinformatics. The expression data of a large number of genes from small numbers of subjects are used to identify inform...
Yue Fan, Mark A. Kon, Shinuk Kim, Charles DeLisi
CIKM
2008
Springer
13 years 10 months ago
Learning the distance metric in a personal ontology
Personal ontology construction is the task of sorting through relevant materials, identifying the main topics and concepts, and organizing them to suit personal needs. Automatic c...
Hui Yang, Jamie Callan
KDD
2007
ACM
154views Data Mining» more  KDD 2007»
14 years 9 months ago
Canonicalization of database records using adaptive similarity measures
It is becoming increasingly common to construct databases from information automatically culled from many heterogeneous sources. For example, a research publication database can b...
Aron Culotta, Michael L. Wick, Robert Hall, Matthe...
RAID
1999
Springer
14 years 29 days ago
IDS Standards: Lessons Learned to Date
: I will discuss two efforts to get Intrusion Detection Systems to work together - the Common Intrusion Detection Framework (CIDF), and the IETF's working group to develop an ...
Stuart Staniford-Chen
EMNLP
2010
13 years 6 months ago
Clustering-Based Stratified Seed Sampling for Semi-Supervised Relation Classification
Seed sampling is critical in semi-supervised learning. This paper proposes a clusteringbased stratified seed sampling approach to semi-supervised learning. First, various clusteri...
Longhua Qian, Guodong Zhou