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ICML
2007
IEEE
15 years 11 months ago
2007
IEEE
We propose a family of clustering algorithms based on the maximization of dependence between the input variables and their cluster labels, as expressed by the Hilbert-Schmidt Inde...
100
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ICML
2007
IEEE
15 years 11 months ago
2007
IEEE
A general framework is proposed for gradient boosting in supervised learning problems where the loss function is defined using a kernel over the output space. It extends boosting ...
108
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ICML
2007
IEEE
15 years 11 months ago
2007
IEEE
We develop a mixture-based approach to robust density modeling and outlier detection for experimental multivariate data that includes measurement error information. Our model is d...
101
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ICML
2007
IEEE
15 years 11 months ago
2007
IEEE
We introduce a framework for filtering features that employs the Hilbert-Schmidt Independence Criterion (HSIC) as a measure of dependence between the features and the labels. The ...
ICML
2007
IEEE
15 years 11 months ago
2007
IEEE
In many real-world applications, Euclidean distance in the original space is not good due to the curse of dimensionality. In this paper, we propose a new method, called Discrimina...