Machine Learning Techniques applied in risk assessment related to food safety
作者： , G. Ru , M.I. Crescio , F. Ingravalle , C. Maurella
DOI: 10.2903/SP.EFSA.2017.EN-1254
关键词: Risk analysis (engineering) 、 Data science 、 Risk assessment 、 Food safety 、 Engineering
摘要:
sci-hub.se 参考文章(194)
J.A.K. Suykens, J. Vandewalle, Least Squares Support Vector Machine Classifiers Neural Processing Letters. ,vol. 9, pp. 293- 300 ,(1999) , 10.1023/A:1018628609742
W. Z. Liu, A. P. White, S. G. Thompson, M. A. Bramer, Techniques for Dealing with Missing Values in Classification intelligent data analysis. pp. 527- 536 ,(1997) , 10.1007/BFB0052868
Michael E Tipping, Sparse bayesian learning and the relevance vector machine Journal of Machine Learning Research. ,vol. 1, pp. 211- 244 ,(2001) , 10.1162/15324430152748236
A. Peters, B. Lausen, G. Michelson, O. Gefeller, Diagnosis of glaucoma by indirect classifiers. Methods of Information in Medicine. ,vol. 42, pp. 99- 103 ,(2003) , 10.1055/S-0038-1634214
Alfred Kar Yin Truong, Fast growing and interpretable oblique trees via logistic regression models University of Oxford. ,(2009)
Charles Sutton, Andrew McCallum, An Introduction to Conditional Random Fields for Relational Learning MIT Press. ,(2007)
R. R. Jackson, A. D. Blest, Short Communication: The Distances at Which a Primitive Jumping Spider, Portia Fimbriata, Makes Visual Discriminations The Journal of Experimental Biology. ,vol. 97, pp. 441- 445 ,(1982)
Charu C. Aggarwal, ChengXiang Zhai, A survey of text classification algorithms Mining Text Data. pp. 163- 222 ,(2012) , 10.1007/978-1-4614-3223-4_6
Sotiris B. Kotsiantis, Supervised Machine Learning: A Review of Classification Techniques Informatica (lithuanian Academy of Sciences). ,vol. 31, pp. 249- 268 ,(2007)