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» Experiments with Classifier Combining Rules
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ECML
2005
Springer
15 years 6 months ago
Error-Sensitive Grading for Model Combination
Abstract. Ensemble learning is a powerful learning approach that combines multiple classifiers to improve prediction accuracy. An important decision while using an ensemble of cla...
Surendra K. Singhi, Huan Liu
ICPR
2004
IEEE
16 years 2 months ago
Two-Stage Classification System combining Model-Based and Discriminative Approaches
For the tasks of classification, two types of patterns can generate problems: ambiguous patterns and outliers. Furthermore, it is possible to separate classification algorithms in...
Jonathan Milgram, Mohamed Cheriet, Robert Sabourin
BMCBI
2010
179views more  BMCBI 2010»
15 years 1 months ago
A semi-supervised learning approach to predict synthetic genetic interactions by combining functional and topological properties
Background: Genetic interaction profiles are highly informative and helpful for understanding the functional linkages between genes, and therefore have been extensively exploited ...
Zhuhong You, Zheng Yin, Kyungsook Han, De-Shuang H...
EACL
2009
ACL Anthology
14 years 11 months ago
Who is "You"? Combining Linguistic and Gaze Features to Resolve Second-Person References in Dialogue
We explore the problem of resolving the second person English pronoun you in multi-party dialogue, using a combination of linguistic and visual features. First, we distinguish gen...
Matthew Frampton, Raquel Fernández, Patrick...
ICPR
2008
IEEE
16 years 2 months ago
Multi-class classification strategies for Fisher scores of gesture and sign sequences
In this work, we propose a multi-class classification strategy based on Fisher kernels. Fisher kernels combine the powers of discriminative and generative classifiers by mapping v...
Lale Akarun, Oya Aran