-
1
-
-
33645505792
-
Convexity, classification, and risk bounds
-
Bartlett, P., Jordan, M., and McAuliffe, J. 2006. Convexity, classification, and risk bounds. J. Am. Statist. Assoc. 101,138-156.
-
(2006)
J. Am. Statist. Assoc
, vol.101
, pp. 138-156
-
-
Bartlett, P.1
Jordan, M.2
McAuliffe, J.3
-
3
-
-
31144459985
-
Oncogenic pathway signatures in human cancers as a guide to targetedtherapies
-
Bild, A., Yao, G., Chang, J. et al. 2006. Oncogenic pathway signatures in human cancers as a guide to targetedtherapies. Nature 439, 353-357.
-
(2006)
Nature
, vol.439
, pp. 353-357
-
-
Bild, A.1
Yao, G.2
Chang, J.3
-
4
-
-
0003802343
-
-
Wadsworth and Brooks,Belmont, CA
-
Breiman, L., Friedman, J.H., Olshen, A. et al. 1984. Classification and Regression Trees. Wadsworth and Brooks,Belmont, CA.
-
(1984)
Classification and Regression Trees
-
-
Breiman, L.1
Friedman, J.H.2
Olshen, A.3
-
5
-
-
7044231546
-
An iterative thresholding algorithm for linear inverse problems with asparsity constraint
-
Daubechies, I., Defrise, M., and De Mol, C. 2004. An iterative thresholding algorithm for linear inverse problems with asparsity constraint. Commun. Pure Appl. Math. 57, 1413-1457.
-
(2004)
Commun. Pure Appl. Math
, vol.57
, pp. 1413-1457
-
-
Daubechies, I.1
Defrise, M.2
De Mol, C.3
-
6
-
-
62549127689
-
Elastic-net regularization in learning theory
-
De Mol, C., De Vito, E., and Rosasco, L. 2009. Elastic-net regularization in learning theory. J.Complexity 25, 201-230.
-
(2009)
J.Complexity
, vol.25
, pp. 201-230
-
-
De Mol, C.1
De Vito, E.2
Rosasco, L.3
-
7
-
-
0036489046
-
Comparison of discrimination methods for the classification of tumorsusing gene expression data
-
Dudoit, S., Fridlyand, J., and Speed, T.P. 2002. Comparison of discrimination methods for the classification of tumorsusing gene expression data. J. Am. Statist. Assoc. 97, 77-87.
-
(2002)
J. Am. Statist. Assoc
, vol.97
, pp. 77-87
-
-
Dudoit, S.1
Fridlyand, J.2
Speed, T.P.3
-
8
-
-
0003531188
-
-
Kluwer, Amsterdam
-
Engl, H.W., Hanke, M., and Neubauer, A. 1996. Regularization of Inverse Problems. Kluwer, Amsterdam.
-
(1996)
Regularization of Inverse Problems
-
-
Engl, H.W.1
Hanke, M.2
Neubauer, A.3
-
9
-
-
2942731012
-
An extensive empirical study of feature selection metrics for text classification
-
Forman, G. 2003. An extensive empirical study of feature selection metrics for text classification. J. Mach. Learn. Res.3, 1289-1306.
-
(2003)
J. Mach. Learn. Res
, vol.3
, pp. 1289-1306
-
-
Forman, G.1
-
10
-
-
0031211090
-
A decision-theoretic generalization of on-line learning and an application toboosting
-
Freund Y., and Schapire, R. 1997. A decision-theoretic generalization of on-line learning and an application toboosting. J. Comput. System Sci. 55, 119-139.
-
(1997)
J. Comput. System Sci
, vol.55
, pp. 119-139
-
-
Freund, Y.1
Schapire, R.2
-
11
-
-
2942602999
-
Entropy-based gene ranking without selection bias for the predictiveclassification of microarray data
-
Furlanello, C., Serafini, M., Merler, S. et al. 2003. Entropy-based gene ranking without selection bias for the predictiveclassification of microarray data. BMC Bioinform. 4, 54.
-
(2003)
BMC Bioinform
, vol.4
, pp. 54
-
-
Furlanello, C.1
Serafini, M.2
Merler, S.3
-
12
-
-
27744565003
-
Classification and selection of biomarkers in genomic data using lasso
-
Ghosh, D., and Chinnaiyan, A.M. 2005. Classification and selection of biomarkers in genomic data using lasso.J. Biomed. Biotechnol. 2, 147-154.
-
(2005)
J. Biomed. Biotechnol
, vol.2
, pp. 147-154
-
-
Ghosh, D.1
Chinnaiyan, A.M.2
-
13
-
-
0033569406
-
Molecular classification of cancer: Class discovery and class predictionby gene expression monitoring
-
Golub, T., Slonim, D., Tamayo, P. et al. 1999. Molecular classification of cancer: class discovery and class predictionby gene expression monitoring. Science 286, 531-537.
-
(1999)
Science
, vol.286
, pp. 531-537
-
-
Golub, T.1
Slonim, D.2
Tamayo, P.3
-
14
-
-
0036735386
-
Translation of microarray data into clinically relevant cancerdiagnostic tests using gene expression ratios in lung cancer and mesothelioma
-
Gordon, R., Jensen, G.J., Hsiao, L.-L., et al. 2002. Translation of microarray data into clinically relevant cancerdiagnostic tests using gene expression ratios in lung cancer and mesothelioma. Cancer Res. 62, 4963-4967.
-
(2002)
Cancer Res
, vol.62
, pp. 4963-4967
-
-
Gordon, R.1
Jensen, G.J.2
Hsiao, L.-L.3
-
15
-
-
33745561205
-
An introduction to variable and feature selection
-
Guyon, I., and Elisseeff, A. 2003. An introduction to variable and feature selection. J. Mach. Learn. Res. 3, 1157-1182.
-
(2003)
J. Mach. Learn. Res
, vol.3
, pp. 1157-1182
-
-
Guyon, I.1
Elisseeff, A.2
-
16
-
-
0036161259
-
Gene selection for cancer classification using support vector machines
-
Guyon, I., Weston, J., Barnhill, S. et al. 2002. Gene selection for cancer classification using support vector machines.Mach. Learn. 46, 389-432.
-
(2002)
Mach. Learn
, vol.46
, pp. 389-432
-
-
Guyon, I.1
Weston, J.2
Barnhill, S.3
-
17
-
-
0003684449
-
-
Springer-Verlag, New York
-
Hastie, T., Tibshirani, R., and Friedman, J. 2001. The Elements of Statistical Learning. Springer-Verlag, New York.
-
(2001)
The Elements of Statistical Learning
-
-
Hastie, T.1
Tibshirani, R.2
Friedman, J.3
-
18
-
-
84942484786
-
Ridge regression: Biased estimation for nonorthogonal problems
-
Hoerl, A.E., and Kennard, R. 1970. Ridge regression: biased estimation for nonorthogonal problems. Technometrics 12, 55-67.
-
(1970)
Technometrics
, vol.12
, pp. 55-67
-
-
Hoerl, A.E.1
Kennard, R.2
-
19
-
-
0031381525
-
Wrappers for feature subset selection
-
Kohavi, R., and John, G. 1997. Wrappers for feature subset selection. Artif. Intellig. 97, 273-324.
-
(1997)
Artif. Intellig
, vol.97
, pp. 273-324
-
-
Kohavi, R.1
John, G.2
-
20
-
-
33846193774
-
A note on the lasso and related procedures in model selection
-
Leng, C., Lin, Y., and Wahba, G. 2006. A note on the lasso and related procedures in model selection. Statist. Sinica 16,1273-1284.
-
(2006)
Statist. Sinica
, vol.16
, pp. 1273-1284
-
-
Leng, C.1
Lin, Y.2
Wahba, G.3
-
21
-
-
49949090353
-
Penalized feature selection and classification in bioinformatics
-
Ma, S., and Huang, J. 2008. Penalized feature selection and classification in bioinformatics. Brief. Bioinform. 9, 392-103.
-
(2008)
Brief. Bioinform
, vol.9
, pp. 392-103
-
-
Ma, S.1
Huang, J.2
-
22
-
-
0025056697
-
Regularization algorithms for learning that are equivalent to multilayer networks
-
Poggio, T., and Girosi, F. 1990.Regularization algorithms for learning that are equivalent to multilayer networks.Science 247, 978-982.
-
(1990)
Science
, vol.247
, pp. 978-982
-
-
Poggio, T.1
Girosi, F.2
-
23
-
-
35748932917
-
A review of feature selection techniques in bioinformatics
-
Saeys, Y., Inza, I., and Larranaga, P. 2007. A review of feature selection techniques in bioinformatics. Bioinformatics23, 2507-2517.
-
(2007)
Bioinformatics
, vol.23
, pp. 2507-2517
-
-
Saeys, Y.1
Inza, I.2
Larranaga, P.3
-
24
-
-
33645581993
-
Microarray gene expression data with linked survival phenotypes: Diffuse large-b-cell lymphoma revisited
-
Segal, M.R. 2006. Microarray gene expression data with linked survival phenotypes: diffuse large-b-cell lymphoma revisited. Biostatistics 7, 268-285.
-
(2006)
Biostatistics
, vol.7
, pp. 268-285
-
-
Segal, M.R.1
-
25
-
-
0742321914
-
Regression approaches for microarray data analysis
-
Segal, M.R., Dahlquist, K.D., and Conklin, B.R. 2003. Regression approaches for microarray data analysis. J. Comput.revisited. Biol,961 980.
-
(2003)
J. Comput.revisited. Biol
, vol.961
, pp. 980
-
-
Segal, M.R.1
Dahlquist, K.D.2
Conklin, B.R.3
-
26
-
-
19044391072
-
Gene expression correlates of clinical prostate cancer behavior
-
Singh, D., Febbo, P.G., Ross, K. et al. 2002. Gene expression correlates of clinical prostate cancer behavior. Cancer Cell 1, 203-209.
-
(2002)
Cancer Cell
, vol.1
, pp. 203-209
-
-
Singh, D.1
Febbo, P.G.2
Ross, K.3
-
27
-
-
0001287271
-
Regression shrinkage and selection via the lasso
-
Tibshirani, R. 1996. Regression shrinkage and selection via the lasso. J. R. Stat. Soc. Ser. B 56, 267-288.
-
(1996)
J. R. Stat. Soc. Ser. B
, vol.56
, pp. 267-288
-
-
Tibshirani, R.1
-
28
-
-
84900616454
-
Kernel methods in genomics and computational biology
-
Camps-Valls, G, Rojo-Alvarez, J.L.,and Martinez-Ramon, M, eds, Idea Group,Hershey, PA
-
Vert, J. 2007. Kernel methods in genomics and computational biology, 42-63. In: Camps-Valls, G., Rojo-Alvarez, J.L.,and Martinez-Ramon, M., eds. Kernel Methods in Bioengineering, Signal and Image Processing. Idea Group,Hershey, PA.
-
(2007)
Kernel Methods in Bioengineering, Signal and Image Processing
, pp. 42-63
-
-
Vert, J.1
-
30
-
-
16244401458
-
Regularization and variable selection via the elastic net
-
Zou, Z., and Hastie, T. 2005. Regularization and variable selection via the elastic net. J. R. Stat. Soc. Ser. B 67, 301-320.
-
(2005)
J. R. Stat. Soc. Ser. B
, vol.67
, pp. 301-320
-
-
Zou, Z.1
Hastie, T.2
|