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» Evaluating algorithms that learn from data streams
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ICML
2006
IEEE
16 years 5 months ago
Learning a kernel function for classification with small training samples
When given a small sample, we show that classification with SVM can be considerably enhanced by using a kernel function learned from the training data prior to discrimination. Thi...
Tomer Hertz, Aharon Bar-Hillel, Daphna Weinshall
ECML
2006
Springer
15 years 8 months ago
Automatically Evolving Rule Induction Algorithms
Research in the rule induction algorithm field produced many algorithms in the last 30 years. However, these algorithms are usually obtained from a few basic rule induction algorit...
Gisele L. Pappa, Alex Alves Freitas
PVLDB
2008
232views more  PVLDB 2008»
15 years 2 months ago
FINCH: evaluating reverse k-Nearest-Neighbor queries on location data
A Reverse k-Nearest-Neighbor (RkNN) query finds the objects that take the query object as one of their k nearest neighbors. In this paper we propose new solutions for evaluating R...
Wei Wu, Fei Yang, Chee Yong Chan, Kian-Lee Tan
WEBDB
2009
Springer
115views Database» more  WEBDB 2009»
15 years 11 months ago
A Machine Learning Approach to Foreign Key Discovery
We study the problem of automatically discovering semantic associations between schema elements, namely foreign keys. This problem is important in all applications where data sets...
Alexandra Rostin, Oliver Albrecht, Jana Bauckmann,...
PKDD
2010
Springer
158views Data Mining» more  PKDD 2010»
15 years 2 months ago
Learning Sparse Gaussian Markov Networks Using a Greedy Coordinate Ascent Approach
In this paper, we introduce a simple but efficient greedy algorithm, called SINCO, for the Sparse INverse COvariance selection problem, which is equivalent to learning a sparse Ga...
Katya Scheinberg, Irina Rish