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. © Springer-Verlag Berlin Heidelberg 2004.
CITATION STYLE
Luo, Z., Bellotti, T., & Gammerman, A. (2004). Qualified predictions for proteomics pattern diagnostics with confidence machines. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3177, 46–51. https://doi.org/10.1007/978-3-540-28651-6_7
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