کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
533313 870100 2013 9 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Hyperdisk based large margin classifier
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
پیش نمایش صفحه اول مقاله
Hyperdisk based large margin classifier
چکیده انگلیسی

We introduce a large margin linear binary classification framework that approximates each class with a hyperdisk – the intersection of the affine support and the bounding hypersphere of its training samples in feature space – and then finds the linear classifier that maximizes the margin separating the two hyperdisks. We contrast this with Support Vector Machines (SVMs), which find the maximum-margin separator of the pointwise convex hulls of the training samples, arguing that replacing convex hulls with looser convex class models such as hyperdisks provides safer margin estimates that improve the accuracy on some problems. Both the hyperdisks and their separators are found by solving simple quadratic programs. The method is extended to nonlinear feature spaces using the kernel trick, and multi-class problems are dealt with by combining binary classifiers in the same ways as for SVMs. Experiments on a range of data sets show that the method compares favourably with other popular large margin classifiers.


► A novel large margin classifier using hyperdisk model has been introduced.
► We proposed two different methods for finding margin separator between hyperdisks.
► Our first method is based on 2D Newton root-finding.
► Our second method uses quadratically constrained quadratic programming.
► The method compares favourably with other popular margin classifiers.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 46, Issue 6, June 2013, Pages 1523–1531
نویسندگان
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