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Tommi Kärkkäinen
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Journal Articles
Publisher: Journals Gateway
Neural Computation (2004) 16 (4): 837–862.
Published: 01 April 2004
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The connection between robust statistical estimates and nonsmooth optimization is established. Based on the resulting family of optimization problems, robust learning problem formulations with regularization-based control on the model complexity of the multilayer perceptron network are described and analyzed. Numerical experiments for simulated regression problems are conducted, and new strategies for determining the regularization coefficient are proposed and evaluated.
Journal Articles
Publisher: Journals Gateway
Neural Computation (2002) 14 (6): 1451–1480.
Published: 01 June 2002
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A simple and general calculus for the sensitivity analysis of a feedforward MLP network in a layer-wise form is presented. Based on the local optimality conditions, some consequences for the least-means-squares learning problem are stated and further discussed. Numerical experiments with formulation and comparison of different weight decay techniques are included.