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» Classifier Selection Based on Data Complexity Measures
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CVPR
2009
IEEE
16 years 5 months ago
An Instance Selection Approach to Multiple Instance Learning
Multiple-instance Learning (MIL) is a new paradigm of supervised learning that deals with the classification of bags. Each bag is presented as a collection of instances from whi...
Zhouyu Fu (Australian National University), Antoni...
INFSOF
2002
108views more  INFSOF 2002»
14 years 9 months ago
Architectural styles for distributed processing systems and practical selection method
The software architecture of a system has influences against various software characteristics of the system such as efficiency, reliability, maintainability, and etc.. For support...
Yoshitomi Morisawa, Katsuro Inoue, Koji Torii

Publication
179views
15 years 1 months ago
AutoSelect: What You Want Is What You Get Real-Time Processing of Visual Attention and Affect
While objects of our focus of attention (“where we are looking at”) and accompanying affective responses to those objects is part of our daily experience, little research exis...
Nikolaus Bee, Helmut Prendinger, Arturo Nakasone, ...
CSB
2005
IEEE
137views Bioinformatics» more  CSB 2005»
15 years 3 months ago
A Learned Comparative Expression Measure for Affymetrix GeneChip DNA Microarrays
Perhaps the most common question that a microarray study can ask is, “Between two given biological conditions, which genes exhibit changed expression levels?” Existing methods...
Will Sheffler, Eli Upfal, John Sedivy, William Sta...
ICDAR
2009
IEEE
15 years 4 months ago
Unsupervised Selection and Discriminative Estimation of Orthogonal Gaussian Mixture Models for Handwritten Digit Recognition
The problem of determining the appropriate number of components is important in finite mixture modeling for pattern classification. This paper considers the application of an unsu...
Xuefeng Chen, Xiabi Liu, Yunde Jia