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Class information adapted kernel for support vector machine

หน่วยงาน Central Queensland University, Australia

รายละเอียด

ชื่อเรื่อง : Class information adapted kernel for support vector machine
นักวิจัย : Imam, Tasadduq. , Tickle, Kevin.
คำค้น : Support vector machines. , Pure basic research. , 970108 Expanding Knowledge in the Information and Computing Sciences. , 970110 Expanding Knowledge in Technology. , 970104 Expanding Knowledge in the Earth Sciences. , 080109 Pattern Recognition and Data Mining. , 089999 Information and Computing Sciences not elsewhere classified. , Kernel functions. , Neural networks (Computer science) , SVM -- Class informed kernel -- RBF -- Sensitivity -- Imbalanced data
หน่วยงาน : Central Queensland University, Australia
ผู้ร่วมงาน : -
ปีพิมพ์ : 2553
อ้างอิง : http://hdl.cqu.edu.au/10018/57531
ที่มา : Imam, T & Tickle, K 2010, 'Class information adapted kernel for support vector machine' in K Wong, B Mendis, U Sumudu & A Bouzerdoum (eds.) n KWK Wong, B Mendis, U. Sumudu & A Bouzerdoum (eds.) Lecture Notes in Computer Science, 2010, Volume 6444, Neural Information Processing. Models and Applications, 17th International Conference on Neural Information Processing (ICONIP 2010), 22–25 Nov 2010, Sydney, Australia, 2010, Springer-Verlag, Germany, pp. 116-123, http://dx.doi.org/10.1007/978-3-642-17534-3_15
ความเชี่ยวชาญ : -
ความสัมพันธ์ : ICONIP 2010 : Neural information processing : theory and algorithms, 17th international conference, proceedings, part II, 20-25 November 2010, Sydney, Australia / K.W. Wong ... [et al.] Heidelberg, Germany : Springer, 2010. p. 116-123 8 pages Refereed 0302-9743 1611-3349 (online) 9783642175336 (online) , ACQUIRE [electronic resource] : Central Queensland University Institutional Repository.
ขอบเขตของเนื้อหา : -
บทคัดย่อ/คำอธิบาย :

This article presents a support vector machine (SVM) learning approach that adapts class information within the kernel computation. Experiments on fifteen publicly available datasets are conducted and the impact of proposed approach for varied settings are observed. It is noted that the new approach generally improves minority class prediction, depicting it as a well-suited scheme for imbalanced data. However, a SVM based customization is also developed that significantly improves prediction performance in terms of different measures. Overall, the proposed method holds promise with potential for future extensions.

บรรณานุกรม :
Imam, Tasadduq. , Tickle, Kevin. . (2553). Class information adapted kernel for support vector machine.
    กรุงเทพมหานคร : Central Queensland University, Australia.
Imam, Tasadduq. , Tickle, Kevin. . 2553. "Class information adapted kernel for support vector machine".
    กรุงเทพมหานคร : Central Queensland University, Australia.
Imam, Tasadduq. , Tickle, Kevin. . "Class information adapted kernel for support vector machine."
    กรุงเทพมหานคร : Central Queensland University, Australia, 2553. Print.
Imam, Tasadduq. , Tickle, Kevin. . Class information adapted kernel for support vector machine. กรุงเทพมหานคร : Central Queensland University, Australia; 2553.