Abstract
We describe an approach to regression based on building a probabilistic model with the aid of visualization. The “stereopsis” data set in the predictive uncertainty challenge is used as a case study, for which we constructed a mixture of neural network experts model. We describe both the ideal Bayesian approach and computational shortcuts required to obtain timely results.
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Murray, I., Snelson, E. (2006). A Pragmatic Bayesian Approach to Predictive Uncertainty. In: Quiñonero-Candela, J., Dagan, I., Magnini, B., d’Alché-Buc, F. (eds) Machine Learning Challenges. Evaluating Predictive Uncertainty, Visual Object Classification, and Recognising Tectual Entailment. MLCW 2005. Lecture Notes in Computer Science(), vol 3944. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11736790_3
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DOI: https://doi.org/10.1007/11736790_3
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-33427-9
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