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» Is Combining Classifiers Better than Selecting the Best One
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JIFS
2008
155views more  JIFS 2008»
13 years 7 months ago
Improving supervised learning performance by using fuzzy clustering method to select training data
The crucial issue in many classification applications is how to achieve the best possible classifier with a limited number of labeled data for training. Training data selection is ...
Donghai Guan, Weiwei Yuan, Young-Koo Lee, Andrey G...
BMCBI
2010
224views more  BMCBI 2010»
13 years 7 months ago
An adaptive optimal ensemble classifier via bagging and rank aggregation with applications to high dimensional data
Background: Generally speaking, different classifiers tend to work well for certain types of data and conversely, it is usually not known a priori which algorithm will be optimal ...
Susmita Datta, Vasyl Pihur, Somnath Datta
ISMIR
2005
Springer
139views Music» more  ISMIR 2005»
14 years 13 days ago
Classifier Combination for Capturing Musical Variation
At its heart, music information retrieval is characterized by the need to find the similarity between pieces of music. However, “similar” does not mean “the same”. Theref...
Jeremy Pickens
RIAO
2007
13 years 8 months ago
Selecting Automatically the Best Query Translations
In order to search corpora written in two or more languages, the simplest and most efficient approach is to translate the query submitted into the required language(s). To achieve...
Pierre-Yves Berger, Jacques Savoy
ICML
2006
IEEE
14 years 7 months ago
Using query-specific variance estimates to combine Bayesian classifiers
Many of today's best classification results are obtained by combining the responses of a set of base classifiers to produce an answer for the query. This paper explores a nov...
Chi-Hoon Lee, Russell Greiner, Shaojun Wang