أعرض تسجيلة المادة بشكل مبسط

dc.creator Pontil, Massimiliano
dc.creator Verri, Alessandro
dc.date 2004-10-20T21:04:01Z
dc.date 2004-10-20T21:04:01Z
dc.date 1997-08-01
dc.date.accessioned 2013-10-09T02:48:39Z
dc.date.available 2013-10-09T02:48:39Z
dc.date.issued 2013-10-09
dc.identifier AIM-1612
dc.identifier CBCL-152
dc.identifier http://hdl.handle.net/1721.1/7246
dc.identifier.uri http://koha.mediu.edu.my:8181/xmlui/handle/1721
dc.description Support Vector Machines (SVMs) perform pattern recognition between two point classes by finding a decision surface determined by certain points of the training set, termed Support Vectors (SV). This surface, which in some feature space of possibly infinite dimension can be regarded as a hyperplane, is obtained from the solution of a problem of quadratic programming that depends on a regularization parameter. In this paper we study some mathematical properties of support vectors and show that the decision surface can be written as the sum of two orthogonal terms, the first depending only on the margin vectors (which are SVs lying on the margin), the second proportional to the regularization parameter. For almost all values of the parameter, this enables us to predict how the decision surface varies for small parameter changes. In the special but important case of feature space of finite dimension m, we also show that there are at most m+1 margin vectors and observe that m+1 SVs are usually sufficient to fully determine the decision surface. For relatively small m this latter result leads to a consistent reduction of the SV number.
dc.format 243488 bytes
dc.format 406239 bytes
dc.format application/postscript
dc.format application/pdf
dc.language en_US
dc.relation AIM-1612
dc.relation CBCL-152
dc.title Properties of Support Vector Machines


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أعرض تسجيلة المادة بشكل مبسط