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» Is Combining Classifiers Better than Selecting the Best One
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ICML
2008
IEEE
15 years 10 months ago
Localized multiple kernel learning
Recently, instead of selecting a single kernel, multiple kernel learning (MKL) has been proposed which uses a convex combination of kernels, where the weight of each kernel is opt...
Ethem Alpaydin, Mehmet Gönen
EMNLP
2007
14 years 11 months ago
LEDIR: An Unsupervised Algorithm for Learning Directionality of Inference Rules
Semantic inference is a core component of many natural language applications. In response, several researchers have developed algorithms for automatically learning inference rules...
Rahul Bhagat, Patrick Pantel, Eduard H. Hovy
BMCBI
2007
105views more  BMCBI 2007»
14 years 9 months ago
Predicting RNA secondary structure by the comparative approach: how to select the homologous sequences
Background: The secondary structure of an RNA must be known before the relationship between its structure and function can be determined. One way to predict the secondary structur...
Stefan Engelen, Fariza Tahi
BMCBI
2006
154views more  BMCBI 2006»
14 years 9 months ago
An improved procedure for gene selection from microarray experiments using false discovery rate criterion
Background: A large number of genes usually show differential expressions in a microarray experiment with two types of tissues, and the p-values of a proper statistical test are o...
James J. Yang, Mark C. K. Yang
BMCBI
2006
94views more  BMCBI 2006»
14 years 9 months ago
Noise-injected neural networks show promise for use on small-sample expression data
Background: Overfitting the data is a salient issue for classifier design in small-sample settings. This is why selecting a classifier from a constrained family of classifiers, on...
Jianping Hua, James Lowey, Zixiang Xiong, Edward R...