-
1
-
-
84882743564
-
-
American Cancer Society. Cancer Facts & Figures 2010. Atlanta: American Cancer Society
-
American Cancer Society. Cancer Facts & Figures 2010. Atlanta: American Cancer Society, 2010.
-
(2010)
-
-
-
2
-
-
84882759926
-
-
National Cancer Institute. Breast Cancer Statistics, USA, 2010, National Cancer Institute, (accessed: 11 Jul 2011)
-
National Cancer Institute. Breast Cancer Statistics, USA, 2010, National Cancer Institute, 2010. http://www.cancer.gov/cancertopics/types/breast (accessed: 11 Jul 2011).
-
(2010)
-
-
-
3
-
-
33845881963
-
Improved breast cancer prognosis through the combination of clinical and genetic markers
-
Sun Y, Goodison S, Li J, et al. Improved breast cancer prognosis through the combination of clinical and genetic markers. Bioinformatics 2007;23:30-7.
-
(2007)
Bioinformatics
, vol.23
, pp. 30-37
-
-
Sun, Y.1
Goodison, S.2
Li, J.3
-
4
-
-
84870547848
-
wFDT-Weighted Fuzzy decision trees for prognosis of breast cancer survivability
-
Roddick JF, Li J, Christen P, Kennedy PJ, eds. Glenelg, South Australia
-
Khan U, Shin H, Choi JP, et al. wFDT-Weighted Fuzzy decision trees for prognosis of breast cancer survivability. In: Roddick JF, Li J, Christen P, Kennedy PJ, eds. Proceedings of the Seventh Australasian Data Mining Conference. Glenelg, South Australia, 2008:141-52.
-
(2008)
Proceedings of the Seventh Australasian Data Mining Conference
, pp. 141-152
-
-
Khan, U.1
Shin, H.2
Choi, J.P.3
-
5
-
-
19344364327
-
Predicting breast cancer survivability: a comparison of three data mining methods
-
Delen D, Walker G, Kadam A. Predicting breast cancer survivability: a comparison of three data mining methods. Artif Int Med 2005;34:113-27.
-
(2005)
Artif Int Med
, vol.34
, pp. 113-127
-
-
Delen, D.1
Walker, G.2
Kadam, A.3
-
6
-
-
0036202506
-
A computer program for period analysis of cancer patient survival
-
Brenner H, Gefeller O, Hakulinen T. A computer program for period analysis of cancer patient survival. Eur J Cancer 2002;38:690-5.
-
(2002)
Eur J Cancer
, vol.38
, pp. 690-695
-
-
Brenner, H.1
Gefeller, O.2
Hakulinen, T.3
-
7
-
-
33744961676
-
Applications of machine learning in cancer prediction and prognosis
-
Cruz JA, Wishart DS. Applications of machine learning in cancer prediction and prognosis. Cancer Inform 2006;2:59-78.
-
(2006)
Cancer Inform
, vol.2
, pp. 59-78
-
-
Cruz, J.A.1
Wishart, D.S.2
-
8
-
-
65449146949
-
Breast cancer survivability via AdaBoost algorithms
-
Warren JR, Yu P, Yearwood J, Patrick JD, eds. Wollongong, NSW, Australia
-
Thongkam J, Xu G, Zhang Y, et al. Breast cancer survivability via AdaBoost algorithms. In: Warren JR, Yu P, Yearwood J, Patrick JD, eds. Proceedings of the Second Australasian Workshop on Health Data and Knowledge Management. Wollongong, NSW, Australia, 2008:55-64.
-
(2008)
Proceedings of the Second Australasian Workshop on Health Data and Knowledge Management
, pp. 55-64
-
-
Thongkam, J.1
Xu, G.2
Zhang, Y.3
-
9
-
-
69249220244
-
Towards breast cancer survivability prediction models through improving training space
-
Thongkam J, Xu G, Zhang Y, et al. Towards breast cancer survivability prediction models through improving training space. Expert Syst Appl 2009;36:12200-09.
-
(2009)
Expert Syst Appl
, vol.36
, pp. 12200-12209
-
-
Thongkam, J.1
Xu, G.2
Zhang, Y.3
-
11
-
-
84859921107
-
A high-performance semi-supervised learning method for text chunking
-
Knight K, Ng HT, Oflazer K, eds. Ann Arbor, Michigan
-
Andoy RK, Zhangz T. A high-performance semi-supervised learning method for text chunking. In: Knight K, Ng HT, Oflazer K, eds. Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics. Ann Arbor, Michigan, 2005:1-9.
-
(2005)
Proceedings of the 43rd Annual Meeting on Association for Computational Linguistics
, pp. 1-9
-
-
Andoy, R.K.1
Zhangz, T.2
-
12
-
-
33749013037
-
Semi-supervised model-based document clustering: a comparative study
-
Zhong S. Semi-supervised model-based document clustering: a comparative study. Mach Learn 2006;65:3-29.
-
(2006)
Mach Learn
, vol.65
, pp. 3-29
-
-
Zhong, S.1
-
14
-
-
19344375744
-
Semi-supervised methods to predict patient survival from gene expression data
-
Bair E, Tibshirani R. Semi-supervised methods to predict patient survival from gene expression data. PLoS Biol 2004;2:0511-22.
-
(2004)
PLoS Biol
, vol.2
, pp. 0511-0522
-
-
Bair, E.1
Tibshirani, R.2
-
16
-
-
78751696355
-
Semi-supervised learning of visual classifiers from web images and text
-
Boutilier C, ed. Pasadena, California, USA
-
Morsillo N, Pal C, Nelson R. Semi-supervised learning of visual classifiers from web images and text. In: Boutilier C, ed. Proceedings of the 21st International Joint Conference on Artificial Intelligence. Pasadena, California, USA, 2009:1169-74.
-
(2009)
Proceedings of the 21st International Joint Conference on Artificial Intelligence
, pp. 1169-1174
-
-
Morsillo, N.1
Pal, C.2
Nelson, R.3
-
19
-
-
84882754020
-
-
Seoul, Korea: TBC
-
Shin H, Kim D, Park K, et al. Breast cancer survivability prediction with surveillance, epidemiology, and end results satabase. Seoul, Korea: TBC, 2011.
-
(2011)
Breast cancer survivability prediction with surveillance, epidemiology, and end results satabase
-
-
Shin, H.1
Kim, D.2
Park, K.3
-
20
-
-
84882755401
-
-
SEER. Surveillance, Epidemiology and End Results program National Cancer Institute, (accessed 11 Jul 2011)
-
SEER. Surveillance, Epidemiology and End Results program National Cancer Institute. 2010. http://www.seer.cancer.gov (accessed 11 Jul 2011).
-
(2010)
-
-
-
21
-
-
58049123638
-
Semi-supervised learning with ensemble learning and graph sharpening
-
Colin F, Kim DS, Lee SY, eds. Daejeon, South Korea
-
Choi I, Shin H. Semi-supervised learning with ensemble learning and graph sharpening. In: Colin F, Kim DS, Lee SY, eds. Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning. Daejeon, South Korea, 2008:172-9.
-
(2008)
Proceedings of the 9th International Conference on Intelligent Data Engineering and Automated Learning
, pp. 172-179
-
-
Choi, I.1
Shin, H.2
-
23
-
-
36549013593
-
Graph sharpening plus graph integration: a synergy that improves protein functional classification
-
Shin H, Lisewski AM, Lichtarge O. Graph sharpening plus graph integration: a synergy that improves protein functional classification. Bioinformatics 2007;23:3217-24.
-
(2007)
Bioinformatics
, vol.23
, pp. 3217-3224
-
-
Shin, H.1
Lisewski, A.M.2
Lichtarge, O.3
-
24
-
-
84880903985
-
Graph-based semi-supervised learning as a generative model
-
Veloso MM, ed. Hyderabad, India
-
He J, Carbonell J, Liu Y. Graph-based semi-supervised learning as a generative model. Veloso MM, ed. In: Proceedings of the 20th International Joint Conference on Artificial Intelligence. Hyderabad, India, 2007:2492-7.
-
(2007)
Proceedings of the 20th International Joint Conference on Artificial Intelligence
, pp. 2492-2497
-
-
He, J.1
Carbonell, J.2
Liu, Y.3
-
25
-
-
33749252873
-
Semi-supervised learning
-
Cambridge, England: The MIT Press
-
Chapelle O, Schölkopf B, Zien A. Semi-supervised learning. Cambridge, England: The MIT Press, 2006:3-14.
-
(2006)
, pp. 3-14
-
-
Chapelle, O.1
Schölkopf, B.2
Zien, A.3
-
26
-
-
64149104410
-
Efficient large margin semi-supervised learning
-
Wang J. Efficient large margin semi-supervised learning. J Mach Learn Res 2007;10:719-42.
-
(2007)
J Mach Learn Res
, vol.10
, pp. 719-742
-
-
Wang, J.1
-
30
-
-
33750525529
-
Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS)
-
Allouche O, Tsoar A, Kadmon R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J Appl Ecol 2006;43:1223-32.
-
(2006)
J Appl Ecol
, vol.43
, pp. 1223-1232
-
-
Allouche, O.1
Tsoar, A.2
Kadmon, R.3
-
31
-
-
33646417252
-
Artificial neural networks
-
Sydenham P, Thorn R, eds. London: John Wiley & Sons Inc
-
Abraham A. Artificial neural networks. Sydenham P, Thorn R, eds. In: Handbook of Measuring System Design. London: John Wiley & Sons Inc, 2005.
-
(2005)
Handbook of Measuring System Design
-
-
Abraham, A.1
-
32
-
-
33847676236
-
Neighborhood property-based pattern selection for support vector machines
-
Shin H, Cho S. Neighborhood property-based pattern selection for support vector machines. Neural Comput 2007;19:816-55.
-
(2007)
Neural Comput
, vol.19
, pp. 816-855
-
-
Shin, H.1
Cho, S.2
|