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» Practical Preference Relations for Large Data Sets
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MLDM
2005
Springer
14 years 2 months ago
Supervised Evaluation of Dataset Partitions: Advantages and Practice
In the context of large databases, data preparation takes a greater importance : instances and explanatory attributes have to be carefully selected. In supervised learning, instanc...
Sylvain Ferrandiz, Marc Boullé
KDD
2008
ACM
120views Data Mining» more  KDD 2008»
14 years 9 months ago
Entity categorization over large document collections
Extracting entities (such as people, movies) from documents and identifying the categories (such as painter, writer) they belong to enable structured querying and data analysis ov...
Arnd Christian König, Rares Vernica, Venkates...
SIGMOD
2008
ACM
131views Database» more  SIGMOD 2008»
14 years 8 months ago
Domain adaptation of information extraction models
Domain adaptation refers to the process of adapting an extraction model trained in one domain to another related domain with only unlabeled data. We present a brief survey of exis...
Rahul Gupta, Sunita Sarawagi
NIPS
2004
13 years 10 months ago
Semi-supervised Learning with Penalized Probabilistic Clustering
While clustering is usually an unsupervised operation, there are circumstances in which we believe (with varying degrees of certainty) that items A and B should be assigned to the...
Zhengdong Lu, Todd K. Leen
VLDB
2002
ACM
154views Database» more  VLDB 2002»
13 years 8 months ago
I/O-Conscious Data Preparation for Large-Scale Web Search Engines
Given that commercial search engines cover billions of web pages, efficiently managing the corresponding volumes of disk-resident data needed to answer user queries quickly is a f...
Maxim Lifantsev, Tzi-cker Chiueh