کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
10325989 677463 2005 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Stochastic complexities of reduced rank regression in Bayesian estimation
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
پیش نمایش صفحه اول مقاله
Stochastic complexities of reduced rank regression in Bayesian estimation
چکیده انگلیسی
Reduced rank regression extracts an essential information from examples of input-output pairs. It is understood as a three-layer neural network with linear hidden units. However, reduced rank approximation is a non-regular statistical model which has a degenerate Fisher information matrix. Its generalization error had been left unknown even in statistics. In this paper, we give the exact asymptotic form of its generalization error in Bayesian estimation, based on resolution of learning machine singularities. For this purpose, the maximum pole of the zeta function for the learning theory is calculated. We propose a new method of recursive blowing-ups which yields the complete desingularization of the reduced rank approximation.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 18, Issue 7, September 2005, Pages 924-933
نویسندگان
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