Rank-based classifiers for extremely high-dimensional gene expression data-Reference-Cited by-同舟云学术

Rank-based classifiers for extremely high-dimensional gene expression data

Published:2016-12-19 Issue:4 Volume:12 Page:917-936
ISSN:1862-5347
Container-title:Advances in Data Analysis and Classification
language:en
Short-container-title:Adv Data Anal Classif

Author:

Lausser Ludwig,Schmid Florian,Schirra Lyn-Rouven,Wilhelm Adalbert F. X.,Kestler Hans A.

Publisher

Springer Science and Business Media LLC

Subject

Applied Mathematics,Computer Science Applications,Statistics and Probability

Link

http://link.springer.com/article/10.1007/s11634-016-0277-3/fulltext.html

Reference27 articles.

1. Bavaud F (2009) Aggregation invariance in general clustering approaches. Adv Data Anal Classif 3(3):205–225

2. Ben-Dor A, Bruhn L, Friedman N, Nachman I, Schummer M, Yakhini Z (2000) Tissue classification with gene expression profiles. J Comput Biol 7(3–4):559–583

3. Bishop CM (2006) Pattern recognition and machine learning (information science and statistics). Springer, New York

4. Breiman L (2001) Random forests. Mach Learn 45(1):5–32

5. Breiman L, Friedman JH, Olshen RA, Stone CJ (1984) Classification and regression trees. The Wadsworth statistics/probability series. Chapman & Hall/CRC, Boca Raton

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