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|Title:||A novel ensemble algorithm for tumor classification||Authors:||Sun, Zhan-Li
Seet Gim Lee, Gerald
|Keywords:||DRNTU::Engineering::Electrical and electronic engineering||Issue Date:||2013||Source:||Sun, Z.-L., Wang, H., Lau, W.-S., Seet, G. L. G., Wang, D., & Lam, K.-M. (2013). A novel ensemble algorithm for tumor classification. 10th International Symposium on Neural Networks, 7952, 292-298.||Abstract:||From the viewpoint of image processing, a spectral feature-based TLS (Tikhonov-regularized least-squares) ensemble algorithm is proposed for tumor classification using gene expression data. In the TLS model, a test sample is represented as a linear combination of atoms of an overcomplete dictionary. Two types of dictionaries, spectral feature-based eigenassays and spectral feature-based metasamples, are proposed for the TLS model. Experimental results on standard databases demonstrate the feasibility and effectiveness of the proposed method.||URI:||https://hdl.handle.net/10356/106080
|DOI:||10.1007/978-3-642-39068-5_36||Fulltext Permission:||none||Fulltext Availability:||No Fulltext|
|Appears in Collections:||EEE Conference Papers|
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