Abstract
In this paper, we focus on the problem of prediction with confidence and describe the recently developed transductive confidence machines (TCM). TCM allows us to make predictions within predefined confidence levels, thus providing a controlled and calibrated classification environment. We apply the TCM to the problem of proteomics pattern diagnostics. We demonstrate that the TCM performs well, yielding accurate, well-calibrated and informative predictions in both online and offline learning settings.
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Luo, Z., Bellotti, T., Gammerman, A. (2004). Qualified Predictions for Proteomics Pattern Diagnostics with Confidence Machines. In: Yang, Z.R., Yin, H., Everson, R.M. (eds) Intelligent Data Engineering and Automated Learning – IDEAL 2004. IDEAL 2004. Lecture Notes in Computer Science, vol 3177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-28651-6_7
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DOI: https://doi.org/10.1007/978-3-540-28651-6_7
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-22881-3
Online ISBN: 978-3-540-28651-6
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