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» Evaluating algorithms that learn from data streams
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108
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ICDM
2003
IEEE
115views Data Mining» more  ICDM 2003»
15 years 5 months ago
On Precision and Recall of Multi-Attribute Data Extraction from Semistructured Sources
Machine learning techniques for data extraction from semistructured sources exhibit different precision and recall characteristics. However to date the formal relationship between...
Guizhen Yang, Saikat Mukherjee, I. V. Ramakrishnan
AROBOTS
2002
115views more  AROBOTS 2002»
15 years 12 days ago
Statistical Learning for Humanoid Robots
The complexity of the kinematic and dynamic structure of humanoid robots make conventional analytical approaches to control increasingly unsuitable for such systems. Learning techn...
Sethu Vijayakumar, Aaron D'Souza, Tomohiro Shibata...
103
Voted
KDD
2004
ACM
117views Data Mining» more  KDD 2004»
16 years 29 days ago
Systematic data selection to mine concept-drifting data streams
One major problem of existing methods to mine data streams is that it makes ad hoc choices to combine most recent data with some amount of old data to search the new hypothesis. T...
Wei Fan
117
Voted
KDD
2003
ACM
194views Data Mining» more  KDD 2003»
16 years 29 days ago
Finding recent frequent itemsets adaptively over online data streams
A data stream is a massive unbounded sequence of data elements continuously generated at a rapid rate. Consequently, the knowledge embedded in a data stream is more likely to be c...
Joong Hyuk Chang, Won Suk Lee
96
Voted
SIGMOD
2010
ACM
213views Database» more  SIGMOD 2010»
15 years 5 months ago
On active learning of record matching packages
We consider the problem of learning a record matching package (classifier) in an active learning setting. In active learning, the learning algorithm picks the set of examples to ...
Arvind Arasu, Michaela Götz, Raghav Kaushik