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129
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EUROCOLT
1999
Springer
15 years 7 months ago
Regularized Principal Manifolds
Many settings of unsupervised learning can be viewed as quantization problems — the minimization of the expected quantization error subject to some restrictions. This allows the ...
Alex J. Smola, Robert C. Williamson, Sebastian Mik...
124
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AIA
2007
15 years 4 months ago
Classification of biomedical high-resolution micro-CT images for direct volume rendering
This paper introduces a machine learning approach into the process of direct volume rendering of biomedical highresolution 3D images. More concretely, it proposes a learning pipel...
Maite López-Sánchez, Jesús Ce...
111
Voted
ICML
2009
IEEE
16 years 3 months ago
Discriminative k-metrics
The k q-flats algorithm is a generalization of the popular k-means algorithm where q dimensional best fit affine sets replace centroids as the cluster prototypes. In this work, a ...
Arthur Szlam, Guillermo Sapiro
135
Voted
ICML
2007
IEEE
16 years 3 months ago
Dimensionality reduction and generalization
In this paper we investigate the regularization property of Kernel Principal Component Analysis (KPCA), by studying its application as a preprocessing step to supervised learning ...
Sofia Mosci, Lorenzo Rosasco, Alessandro Verri
140
Voted
ICML
2007
IEEE
16 years 3 months ago
Gradient boosting for kernelized output spaces
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 ...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...