-
1
-
-
36948999941
-
-
Irvine, CA: University of California, Department of Information and Computer Science
-
Asuncion, A. and D. J. Newman. 2007. UCI Machine Learning Repository [http://www.ics.uci.edu/~mlearn/MLRepository.html]. Irvine, CA: University of California, Department of Information and Computer Science.
-
(2007)
UCI Machine Learning Repository
-
-
Asuncion, A.1
Newman, D.J.2
-
2
-
-
76549106519
-
Visual detection of blemishes in potatoes using minimalist boosted classifiers
-
Barnes, M., T. Duckett, G. Cielniak, G. Stround, and G. Harper. 2010. Visual detection of blemishes in potatoes using minimalist boosted classifiers. Journal of Food Engineering 98(3): 339-346.
-
(2010)
Journal of Food Engineering
, vol.98
, Issue.3
, pp. 339-346
-
-
Barnes, M.1
Duckett, T.2
Cielniak, G.3
Stround, G.4
Harper, G.5
-
3
-
-
4544345162
-
Pizza sauce spread classification using color vision and support vector machines
-
Du, C. J., and D. W. Sun. 2005. Pizza sauce spread classification using color vision and support vector machines. Journal of Food Engineering 66: 137-145.
-
(2005)
Journal of Food Engineering
, vol.66
, pp. 137-145
-
-
Du, C.J.1
Sun, D.W.2
-
5
-
-
0033281701
-
Improved boosting algorithms using confidence-rated predictions
-
Freund, Y., and R. E. Schapire. 1999a. Improved boosting algorithms using confidence-rated predictions. Machine Learning 37(3): 297-336.
-
(1999)
Machine Learning
, vol.37
, Issue.3
, pp. 297-336
-
-
Freund, Y.1
Schapire, R.E.2
-
7
-
-
0034164230
-
Additive logistic regression: A statistical view of boosting
-
Friedman, J., T. Hastie, and R. Tibshirani. 2000. Additive logistic regression: A statistical view of boosting. The Annals of Statistics 28(2): 337-374.
-
(2000)
The Annals of Statistics
, vol.28
, Issue.2
, pp. 337-374
-
-
Friedman, J.1
Hastie, T.2
Tibshirani, R.3
-
8
-
-
33947168099
-
Crop verses weed recognition with artificial neural networks
-
St. Joseph, Mich.:ASAE
-
Gliever, C., and D. C. Slaughter. 2001. Crop verses weed recognition with artificial neural networks. ASAE Meeting Paper No. 01-3104. St. Joseph, Mich.:ASAE.
-
(2001)
ASAE Meeting Paper No. 01-3104
-
-
Gliever, C.1
Slaughter, D.C.2
-
10
-
-
77949487615
-
Development of soft computing and applications in agricultural and biological engineering
-
Huang, Y., Y. Lan, S. J. Thomson, A. Fang, W. C. Hoffmann, and R.E. Lace. 2010. Development of soft computing and applications in agricultural and biological engineering. Computers and Electronics in Agriculture 71: 107-127.
-
(2010)
Computers and Electronics in Agriculture
, vol.71
, pp. 107-127
-
-
Huang, Y.1
Lan, Y.2
Thomson, S.J.3
Fang, A.4
Hoffmann, W.C.5
Lace, R.E.6
-
11
-
-
55449125185
-
Support Vector Machines and Kernels for Computational Biology
-
Available at Accessed on Accessed on 30 April 2010
-
Hur, A. B., C. S. Ong, S. Sonnenburg, B. Scholkopf, and G. Rätsch. 2008. Support Vector Machines and Kernels for Computational Biology. PLoS Computational Biology. Available at http://www.ncbi.nlm.nih.gov/pmc/articles/ PMC2547983/pdf/pcbi.1000173.pdf. Accessed on Accessed on 30 April 2010.
-
(2008)
PLoS Computational Biology
-
-
Hur, A.B.1
Ong, C.S.2
Sonnenburg, S.3
Scholkopf, B.4
Rätsch, G.5
-
12
-
-
32644445913
-
Application of support vector machine technology for weed and nitrogen stress detection in corn
-
DOI 10.1016/j.compag.2005.12.001, PII S0168169906000111
-
Karimi, Y., S. O. Prasher, R.M. Patel, and S.H. Kim. 2006. Application of support vector machine technology for weed and nitrogen stress detection in corn. Computers and Electronics in Agriculture 51(1-2): 99-109. (Pubitemid 43242936)
-
(2006)
Computers and Electronics in Agriculture
, vol.51
, Issue.1-2
, pp. 99-109
-
-
Karimi, Y.1
Prasher, S.O.2
Patel, R.M.3
Kim, S.H.4
-
14
-
-
78649708972
-
Local adaptive thresholding and adaboost classifier for automated x-ray imaging system for pecan defect detection
-
Stillwater, OK: Food and Agricultural Products Center, Oklahoma State University
-
Mathanker, S. K., P. R. Weckler, T. Bowser, N. Wang, N. O. Maness, G. Fan, and M. Stone. 2010. Local adaptive thresholding and adaboost classifier for automated x-ray imaging system for pecan defect detection. FAPC/IFT-OK Research Symposium, Stillwater, OK: Food and Agricultural Products Center, Oklahoma State University.
-
(2010)
FAPC/IFT-OK Research Symposium
-
-
Mathanker, S.K.1
Weckler, P.R.2
Bowser, T.3
Wang, N.4
Maness, N.O.5
Fan, G.6
Stone, M.7
-
15
-
-
63149134463
-
Canola - Weed identification for machine vision based patch spraying
-
St. Joseph, Mich.: ASABE
-
Mathanker, S. K., P. R. Weckler, and R. K. Taylor. 2008. Canola - weed identification for machine vision based patch spraying. ASABE Meeting Paper No. 085134. St. Joseph, Mich.: ASABE.
-
(2008)
ASABE Meeting Paper No. 085134
-
-
Mathanker, S.K.1
Weckler, P.R.2
Taylor, R.K.3
-
17
-
-
35248862907
-
An introduction to boosting and leveraging
-
Berlin: Springer
-
Meir, R. and G. Rätsch. 2003. An introduction to boosting and leveraging: In Advanced Lectures on Machine Learning 118-183. Berlin: Springer.
-
(2003)
Advanced Lectures on Machine Learning
, pp. 118-183
-
-
Meir, R.1
Rätsch, G.2
-
21
-
-
78149343462
-
A computer vision approach for weeds identification through support vector machines
-
In Press, Corrected Proof
-
Tellaeche, A., G. Pajares, X. P. Burgos-Artizzu and A. Ribeiro. 2010. A computer vision approach for weeds identification through support vector machines.Applied Soft Computing In Press, Corrected Proof.
-
(2010)
Applied Soft Computing
-
-
Tellaeche, A.1
Pajares, G.2
Burgos-Artizzu, X.P.3
Ribeiro, A.4
-
24
-
-
76549131104
-
-
Available at: Accessed on 2 October 2009
-
Vezhnevets, A. 2006. GML AdaBoost MATLAB Toolbox. Available at: 〈http://research.graphicon.ru〉. Accessed on 2 October 2009.
-
(2006)
GML AdaBoost MATLAB Toolbox
-
-
Vezhnevets, A.1
|