Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction
Joint Authors
Blanco, Ángela
Martín-Merino, Manuel
De Las Rivas, Javier
Source
Issue
Vol. 2009, Issue 2009 (31 Dec. 2009), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2009-05-10
Country of Publication
Egypt
No. of Pages
9
Main Subjects
Abstract EN
DNA microarrays provide rich profiles that are used in cancer prediction considering the gene expression levels across a collection of related samples.
Support Vector Machines (SVM) have been applied to the classification of cancer samples with encouraging results.
However, they rely on Euclidean distances that fail to reflect accurately the proximities among sample profiles.
Then, non-Euclidean dissimilarities provide additional information that should be considered to reduce the misclassification errors.
In this paper, we incorporate in the ν-SVM algorithm a linear combination of non-Euclidean dissimilarities.
The weights of the combination are learnt in a (Hyper Reproducing Kernel Hilbert Space) HRKHS using a Semidefinite Programming algorithm.
This approach allows us to incorporate a smoothing term that penalizes the complexity of the family of distances and avoids overfitting.
The experimental results suggest that the method proposed helps to reduce the misclassification errors in several human cancer problems.
American Psychological Association (APA)
Martín-Merino, Manuel& Blanco, Ángela& De Las Rivas, Javier. 2009. Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction. BioMed Research International،Vol. 2009, no. 2009, pp.1-9.
https://search.emarefa.net/detail/BIM-988470
Modern Language Association (MLA)
Martín-Merino, Manuel…[et al.]. Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction. BioMed Research International No. 2009 (2009), pp.1-9.
https://search.emarefa.net/detail/BIM-988470
American Medical Association (AMA)
Martín-Merino, Manuel& Blanco, Ángela& De Las Rivas, Javier. Combining Dissimilarities in a Hyper Reproducing Kernel Hilbert Space for Complex Human Cancer Prediction. BioMed Research International. 2009. Vol. 2009, no. 2009, pp.1-9.
https://search.emarefa.net/detail/BIM-988470
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-988470