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» Feature selection based on the training set manipulation
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ECIR
2010
Springer
13 years 9 months ago
Learning to Select a Ranking Function
Abstract. Learning To Rank (LTR) techniques aim to learn an effective document ranking function by combining several document features. While the function learned may be uniformly ...
Jie Peng, Craig Macdonald, Iadh Ounis
SISAP
2010
IEEE
259views Data Mining» more  SISAP 2010»
13 years 5 months ago
kNN based image classification relying on local feature similarity
In this paper, we propose a novel image classification approach, derived from the kNN classification strategy, that is particularly suited to be used when classifying images descr...
Giuseppe Amato, Fabrizio Falchi
NIPS
2007
13 years 9 months ago
Sparse Feature Learning for Deep Belief Networks
Unsupervised learning algorithms aim to discover the structure hidden in the data, and to learn representations that are more suitable as input to a supervised machine than the ra...
Marc'Aurelio Ranzato, Y-Lan Boureau, Yann LeCun
ICPR
2006
IEEE
14 years 8 months ago
Vessel Segmentation in 2D-Projection Images Using a Supervised Linear Hysteresis Classifier
2D projection imaging is a widely used procedure for vessel visualization. For the subsequent analysis of the vasculature, precise measurements of e.g. vessel area, vessel length ...
Alexandru Condurache, Til Aach
CVPR
2005
IEEE
14 years 1 months ago
Mapping Low-Level Features to High-Level Semantic Concepts in Region-Based Image Retrieval
In this a novel supervised learning method is proposed to map low-level visualfeatures to high-level semantic conceptsfor region-based image retrieval. The contributions of thispa...
Wei Jiang, Kap Luk Chan, Mingjing Li, HongJiang Zh...