M
Marina Skurichina
Researcher at Delft University of Technology
Publications - 25
Citations - 1694
Marina Skurichina is an academic researcher from Delft University of Technology. The author has contributed to research in topics: Linear discriminant analysis & Boosting (machine learning). The author has an hindex of 17, co-authored 25 publications receiving 1594 citations.
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Journal ArticleDOI
Bagging, Boosting and the Random Subspace Method for Linear Classifiers
TL;DR: Simulation studies show that the performance of the combining techniques is strongly affected by the small sample size properties of the base classifier: boosting is useful for large training sample sizes, while bagging and the random subspace method are useful for criticalTraining sample sizes.
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An experimental study on diversity for bagging and boosting with linear classifiers
TL;DR: Diversity measures indicated that Boosting succeeds in inducing diversity even for stable classifiers whereas Bagging does not, confirming in a quantitative way the intuitive explanation behind the success of Boosting for linear classifiers for increasing training sizes, and the poor performance of Bagging.
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Autofluorescence and Diffuse Reflectance Spectroscopy for Oral Oncology
Diana C.G. de Veld,Marina Skurichina,Max J. H. Witjes,Robert P. W. Duin,Henricus J. C. M. Sterenborg,J.L.N. Roodenburg +5 more
TL;DR: The contributions of diffuse reflectance and autofluorescence spectroscopy to diagnostic performance are determined in the present study.
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Bagging for linear classifiers
TL;DR: Bagging (bootstrapping and aggregating) is studied for linear classifiers and it is shown experimentally that bagging might improve the performance of the classifier only for very unstable situations.
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Autofluorescence Characteristics of Healthy Oral Mucosa at Different Anatomical Sites
Diana C.G. de Veld,Marina Skurichina,Max J. H. Witjes,Robert P. W. Duin,D. Sterenborg,Willem M. Star,J.L.N. Roodenburg +6 more
TL;DR: The influence of anatomical location on healthy mucosa autofluorescence is investigated and the reliability of this tool for oral cancer detection is improved by using a reference database of spectra fromhealthy mucosa.