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dc.contributor.authorZhao, Yanen_US
dc.date.accessioned2012-06-28T20:57:42Z
dc.date.available2016-12-30T06:46:53Z
dc.date.issued2011-08-31en_US
dc.identifier.otherbibid: 7745457
dc.identifier.urihttps://hdl.handle.net/1813/29531
dc.description.abstractPatents as an important component belonging to innovation can serve as an index in representing the technological development level in a given industry. However, agricultural biotechnology patent (ABP) data have not been updated since 2000. The major objective of this thesis is to identify ABP issued between 2001 and 2007. Apart from traditional manual identifying methods, we focus on adopting a model built on machine learning methodology. As a result, a score will be generated for each patent to indicate its possible inclusion in the agricultural biotechnology category. We select patents based on ranking their scores and following the patent classification scheme. The analysis of 9,539 ABP issued between 2001 and 2007 shows that the quantity reaches its peak value in 2001 and follows a downward trend until 2006. Based on the patent identification result, we also run several economic analyses to verify the ABP development trend and investigate their characteristics.en_US
dc.language.isoen_USen_US
dc.subjectagriculturalen_US
dc.subjectbiotechnologyen_US
dc.subjectpatenten_US
dc.subjectclassificationen_US
dc.subjectmachine learningen_US
dc.titleAutomatic Patent Classification Using Support Vector Machines And Its Applicationsen_US
dc.typedissertation or thesisen_US
thesis.degree.disciplineAgricultural Economics
thesis.degree.grantorCornell Universityen_US
thesis.degree.levelMaster of Science
thesis.degree.nameM.S., Agricultural Economics
dc.contributor.chairGomes, Carla Pen_US
dc.contributor.committeeMemberLesser, William Henrien_US


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