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» Top-k learning to rank: labeling, ranking and evaluation
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ECML
2007
Springer
15 years 3 months ago
Fast Optimization Methods for L1 Regularization: A Comparative Study and Two New Approaches
L1 regularization is effective for feature selection, but the resulting optimization is challenging due to the non-differentiability of the 1-norm. In this paper we compare state...
Mark Schmidt, Glenn Fung, Rómer Rosales
RIAO
2007
14 years 11 months ago
From Layout to Semantic: a Reranking Model for Mapping Web Documents to Mediated XML Representations
Many documents on the Web are formated in a weakly structured format. Because of their weak semantic and because of the heterogeneity of their formats, the information conveyed by...
Guillaume Wisniewski, Patrick Gallinari
VIP
2003
14 years 11 months ago
Optimal Selection of Image Segmentation Algorithms Based on Performance Prediction
Using different algorithms to segment different images is a quite straightforward strategy for automated image segmentation. But the difficulty of the optimal algorithm selection ...
Yong Xia, David Dagan Feng, Rongchun Zhao
CVPR
2001
IEEE
15 years 11 months ago
Learning Similarity Measure for Natural Image Retrieval with Relevance Feedback
A new scheme of learning similarity measure is proposed for content-based image retrieval (CBIR). It learns a boundary that separates the images in the database into two parts. Im...
Guodong Guo, Anil K. Jain, Wei-Ying Ma, HongJiang ...
ICPR
2008
IEEE
15 years 10 months ago
Learning weighted distances for relevance feedback in image retrieval
We present a new method for relevance feedback in image retrieval and a scheme to learn weighted distances which can be used in combination with different relevance feedback metho...
Enrique Vidal, Hermann Ney, Roberto Paredes, Thoma...