Abstract
This paper presents a novel method of selective sampling using conditional class probabilities estimated from a network referred to as the class probability output network (CPON). For selective sampling, an uncertainty measure is defined using the confidence level for the CPON output. As a result, the proposed uncertainty measure represents how confident the CPON output is. We compared the recognition performance between other sampling methods and the proposed one. The relationship between the uncertainty measure and recognition rate was also investigated.
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Kim, HG., Kil, R.M., Lee, SY. (2011). Uncertainty Measure for Selective Sampling Based on Class Probability Output Networks. In: Lu, BL., Zhang, L., Kwok, J. (eds) Neural Information Processing. ICONIP 2011. Lecture Notes in Computer Science, vol 7064. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24965-5_87
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DOI: https://doi.org/10.1007/978-3-642-24965-5_87
Publisher Name: Springer, Berlin, Heidelberg
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