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Features as Sufficient Statistics
- , Sufficient Statistics and Features As above, the image I is the realization of a random process
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O Kaynak, S. Tosunolu, M. Ang. Jr. (Eds.) Recent Advances in Mechatronics, pp.
- Figures 2.2.3 and 2.2.4 show the boxplots of two of these features (sufficient statistics 19 and 23
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Accelerated training of conditional random fields with stochastic gradient methods
- )], etc. (4) The sufficient statistics φ(x, y) represent salient features of the data, and are typically
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Thin Junction Trees
- " or "sufficient statistic" f : X ! R F , where F is the dimensionality of the feature space. The feature f can
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Piecewise pseudolikelihood for efficient CRF training
- or sufficient statistics over the variables in the set a. The parameters of the model are the set Λ = {λa} A a=1
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Informative Features in Vision and Learning
- Inequality ..... 23 2.5 Summary ................................ 25 Features and Sufficient Statistics 26
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Efficient Exact Inference in Planar Ising Models
- , the disagreement costs Eij in (2) are computed as inner products between features (sufficient statistics) x
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Policy-Gradients for PSRs and POMDPs
- )V(s ′ ). (3) Now b is the basis feature, or sufficient statistic, that reveals information about the current
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