Abstract
Data Y = {yt : t = 1, 2, . . ., n}, or Y|X = {(y t , x1,t, x2,t, . . .)}, X explanatory variables. Want to learn properties in Y expressed by set of distributions as models: f(Y|X s ;θ, s), where θ = θ1, . . . , θk(s) real-valued parameters, s structure parameter: for picking the most important variables in X.
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© 2011 Springer-Verlag Berlin Heidelberg
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Rissanen, J. (2011). Optimal Estimation. In: Kivinen, J., Szepesvári, C., Ukkonen, E., Zeugmann, T. (eds) Algorithmic Learning Theory. ALT 2011. Lecture Notes in Computer Science(), vol 6925. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-24412-4_4
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DOI: https://doi.org/10.1007/978-3-642-24412-4_4
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-24411-7
Online ISBN: 978-3-642-24412-4
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