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» Selection of Subsets of Ordered Features in Machine Learning
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123
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ICML
2010
IEEE
15 years 2 months ago
From Transformation-Based Dimensionality Reduction to Feature Selection
Many learning applications are characterized by high dimensions. Usually not all of these dimensions are relevant and some are redundant. There are two main approaches to reduce d...
Mahdokht Masaeli, Glenn Fung, Jennifer G. Dy
107
Voted
ISMB
1993
15 years 3 months ago
Knowledge-Based Generation of Machine-Learning Experiments: Learning with DNA Crystallography Data
Thoughit has been possible in the past to learn to predict DNAhydration patterns from crystallographic data, there is ambiguity in the choice of training data (both in terms of th...
Dawn M. Cohen, Casimir A. Kulikowski, Helen Berman
117
Voted
CLEF
2007
Springer
15 years 8 months ago
MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation
This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image...
Sara Lana-Serrano, Julio Villena-Román, Jos...
109
Voted
CAIP
1999
Springer
139views Image Analysis» more  CAIP 1999»
15 years 6 months ago
Image Retrieval System Based on Machine Learning and Using Color Features
We describe an interactive system for content based image retrieval. The system presents the user with 15 randomly selected images from the database. The user grades the images wit...
Janez Demsar, Dragan Radolovic, Franc Solina
ECML
2006
Springer
15 years 5 months ago
Evaluating Feature Selection for SVMs in High Dimensions
We perform a systematic evaluation of feature selection (FS) methods for support vector machines (SVMs) using simulated high-dimensional data (up to 5000 dimensions). Several findi...
Roland Nilsson, José M. Peña, Johan ...