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» Evaluating learning algorithms and classifiers
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BIBE
2007
IEEE
167views Bioinformatics» more  BIBE 2007»
15 years 3 months ago
Assessing the Performance of Macromolecular Sequence Classifiers
Machine learning approaches offer some of the most cost-effective approaches to building predictive models (e.g., classifiers) in a broad range of applications in computational bio...
Cornelia Caragea, Jivko Sinapov, Vasant Honavar, D...
BMCBI
2006
158views more  BMCBI 2006»
14 years 11 months ago
Parallelization of multicategory support vector machines (PMC-SVM) for classifying microarray data
Background: Multicategory Support Vector Machines (MC-SVM) are powerful classification systems with excellent performance in a variety of data classification problems. Since the p...
Chaoyang Zhang, Peng Li, Arun Rajendran, Youping D...
ICML
2002
IEEE
16 years 16 days ago
Is Combining Classifiers Better than Selecting the Best One
We empirically evaluate several state-of-theart methods for constructing ensembles of heterogeneous classifiers with stacking and show that they perform (at best) comparably to se...
Saso Dzeroski, Bernard Zenko
CORR
2002
Springer
142views Education» more  CORR 2002»
14 years 11 months ago
Learning Algorithms for Keyphrase Extraction
Many academic journals ask their authors to provide a list of about five to fifteen keywords, to appear on the first page of each article. Since these key words are often phrases ...
Peter D. Turney
IJDAR
2002
116views more  IJDAR 2002»
14 years 11 months ago
Performance evaluation of pattern classifiers for handwritten character recognition
Abstract. This paper describes a performance evaluation study in which some efficient classifiers are tested in handwritten digit recognition. The evaluated classifiers include a s...
Cheng-Lin Liu, Hiroshi Sako, Hiromichi Fujisawa