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Abstract: In this paper, we discuss the problem of active training data selection in the presence of noise. We formalize the learning problem in neural networks as an inverse problem using a functional analytic framework and use the Averaged Projection criterion as our optimization criterion for learning. Based on the above framework, we look at training data selection from two objectives, namely, improving the generalization ability and secondly, reducing the noise variance in order to achieve better... (Update)
Context of citations to this paper: More
...the bias is explicitly evaluated by utilizing the knowledge of the distribution of the learning target functions. Vijayakumar, Sugiyama, and Ogawa (1998) extended the condition to the noisy case by dividing the sampling scheme into two stages. The first stage is for reducing...
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BibTeX entry: (Update)
Vijayakumar, S., Sugiyama, M., & Ogawa, H. (1998). Training data selection for optimal generalization with noise variance reduction in neural networks. In M. Marinaro & R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-98 (pp. 153--166). Springer-Verlag. http://citeseer.ist.psu.edu/article/vijayakumar98training.html More
@misc{ vijayakumar98training,
author = "S. Vijayakumar and M. Sugiyama and H. Ogawa",
title = "Training data selection for optimal generalization with noise variance
reduction in neural networks",
text = "Vijayakumar, S., Sugiyama, M., & Ogawa, H. (1998). Training data selection
for optimal generalization with noise variance reduction in neural networks.
In M. Marinaro & R. Tagliaferri (Eds.), Neural Nets WIRN Vietri-98 (pp.
153--166). Springer-Verlag.",
year = "1998",
url = "citeseer.ist.psu.edu/article/vijayakumar98training.html" }
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