کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
408591 679036 2007 11 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Synthesis of maximum margin and multiview learning using unlabeled data
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Synthesis of maximum margin and multiview learning using unlabeled data
چکیده انگلیسی

In this paper we show that the semi-supervised learning with two input sources can be transformed into a maximum margin problem to be similar to a binary support vector machine. Our formulation exploits the unlabeled data to reduce the complexity of the class of the learning functions. In order to measure how the complexity is decreased we use the Rademacher complexity theory. The corresponding optimization problem is convex and it is efficiently solvable for large-scale applications as well.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 70, Issues 7–9, March 2007, Pages 1254–1264
نویسندگان
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