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COOPIS
2004
IEEE
13 years 11 months ago
Learning Classifiers from Semantically Heterogeneous Data
Semantically heterogeneous and distributed data sources are quite common in several application domains such as bioinformatics and security informatics. In such a setting, each dat...
Doina Caragea, Jyotishman Pathak, Vasant Honavar
KDD
2009
ACM
227views Data Mining» more  KDD 2009»
14 years 8 months ago
Efficiently learning the accuracy of labeling sources for selective sampling
Many scalable data mining tasks rely on active learning to provide the most useful accurately labeled instances. However, what if there are multiple labeling sources (`oracles...
Pinar Donmez, Jaime G. Carbonell, Jeff Schneider
TLT
2008
93views more  TLT 2008»
13 years 7 months ago
What Do You Prefer? Using Preferences to Enhance Learning Technology
While the growing number of learning resources increases the choice for learners on how, what and when to learn, it also makes it more and more difficult to find the learning resou...
Philipp Kärger, Daniel Olmedilla, Fabian Abel...
VLDB
2001
ACM
92views Database» more  VLDB 2001»
13 years 12 months ago
Fast Evaluation Techniques for Complex Similarity Queries
Complex similarity queries, i.e., multi-feature multi-object queries, are needed to express the information need of a user against a large multimedia repository. Even if a user in...
Klemens Böhm, Michael Mlivoncic, Hans-Jö...
VLDB
2007
ACM
107views Database» more  VLDB 2007»
14 years 7 months ago
VGRAM: Improving Performance of Approximate Queries on String Collections Using Variable-Length Grams
Many applications need to solve the following problem of approximate string matching: from a collection of strings, how to find those similar to a given string, or the strings in ...
Chen Li, Bin Wang, Xiaochun Yang