J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14:1080--1100, 1986.  Home/Search Document Not in Database Summary Related Articles Check |
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The Speed Prior: A New Simplicity Measure Yielding Near-Optimal .. - Schmidhuber (2002) (3 citations) (Correct)
....of recursiveness leads to a slightly more computable approach than the more general case. However, while M is enumerable, it is not recursive, and thus practically infeasible. This drawback inspired less general yet practically more feasible principles of minimum description length (MDL) [27, 17] as well as priors derived from time bounded restrictions [15] of Kolmogorov complexity [12, 24, 4] No particular instance of these approaches, however, is universally accepted or has a general convincing motivation that carries beyond rather specialized application scenarios. For instance, ....
J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.
Iterated Logarithmic Expansions of the Pathwise Code Lengths for.. - Li, Zu (2000) (2 citations) (Correct)
....Berkeley, CA 94720. L. Li is Supported by the NSF grant DMS 9971698, and B. Yu is supported by the grants NSF DMS 9803063 and ARO DAAG55 98 1 0341. 1 Introduction and background The Minimum Description Length (MDL) principle is introduced by Rissanen as a fundamental principle to model data, see [15, 17] and the reference list in [18] If we encode data from a source by prefix codes, the best code is the one that achieves the minimum description length among all prefix codes if there is such a one. Because of the equivalence between a prefix codelength and the negative logarithm of the ....
....and 0 is an interior point of Theta. Then the codelength of the optimal code is given by Shannon s coding theorem as L 0 = Gamma i=1 log p(X i j 0 ) The codelength corresponding to a given distribution Q(x) is LQ = Gamma i=1 log q(X i ) and the redundancy is RQ = LQ Gamma L 0 . Rissanen [17] shows that for each positive number ffl and for all 0 2 Theta, except in a set whose volume goes to zero as n Gamma 1, E P 0 RQ d Gamma ffl log n: Later Barron and Hengartner ( 2] prove that except for a set of parameter values with a volume zero, lim n 1 EP 0 RQ 2 log n 1 : ....
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J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14:1080--1100, 1986.
The New AI: General & Sound & Relevant for Physics - Schmidhuber (2003) (Correct)
....Fastest Way of Describing Objects Unfortunately, while M and the more general priors of Section 4 are computable in the limit, they are not recursive, and thus practically infeasible. This drawback inspired less general yet practically more feasible principles of minimum description length (MDL) [68, 41] as well as priors derived from time bounded restrictions [31] of Kolmogorov complexity [28, 59, 8] No particular instance of these approaches, however, is universally accepted or has a general convincing motivation that carries beyond rather specialized application scenarios. For instance, ....
J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.
The Speed Prior: A New Simplicity Measure Yielding Near-Optimal .. - Schmidhuber (2002) (3 citations) (Correct)
....of recursiveness leads to a slightly more computable approach than the more general case. However, while M is enumerable, it is not recursive, and thus practically infeasible. This drawback inspired less general yet practically more feasible prin ciples of minimum description length (MDL) [27, 17] as well as priors derived from time bounded restrictions [15] of Kolmogorov complexity [12, 24, 4] No par ticular instance of these approaches, however, is universally accepted or has a general convincing motivation that carries beyond rather specialized application scenarios. For instance, ....
J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.
A New Approximation Method of the Quadratic Discriminant.. - Shin'ichiro Omachi Fang (2000) (1 citation) (Correct)
.... Moreover, since SQDF is derived from the maximum likelihood estimation, it is not only appropriate as a classifier, but it also can be used for model complexity identification with information criterion such as Akaike s Information Criterion (AIC) 13] or Minimum Description Length (MDL) [14]. 2.3 Model Identification In SQDF, the only parameter that is not determined is k, that is, the number of reliable eigenvalues. The other parameters are calculated automatically with samples. Of course, k can be chosen arbitrarily or experimentally. In recognition systems which handle large ....
Rissanen, J.: Stochastic Complexity and Modeling. The Annals of Statistics 14 (1986) 1080--1100
The Specialized Mappings Architecture - Rosales, Sclaroff (2003) (Correct)
....output consisted of 20 joint angles of a human hand linearly encoded by nine values using Principal Component Analysis (PCA) In this experiment, the number of specialized functions was set to 20. This number was found to be optimal in the sense of the Minimum Description Length (MDL) principle [32]; an exhaustive search is impractical, so we find this number via approximate search. Each mapping function was a one hidden layer, feed forward network (multi layer perceptron) with seven hidden neurons. To measure the accuracy of the hand pose reconstruction, we randomly selected approximately ....
J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14,1080-1100, 1986.
Minimum Description Length Principle - Bouckaert (1994) (Correct)
....this ordering. In [3] an 6 algorithm has been presented for optimizing an ordering for this purpose of removing arcs from a given network structure. 4 A Minimum Description Length Approach Another way to judge the quality of a network structure is by the minimum description length principle [18, 19] which stems from coding theory where the aim is to create a network structure that describes the database as accurately as possible with as few symbols as possible. 4.1 The MDL Measure The MDL principle results in the following measure. Definition 4.1 Let U , B S , D, N , n, r i , N ijk , and ....
J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14(3):1080-- 1100, 1986.
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J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14:1080--1100, 1986.
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J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14:1080--1100, 1986.
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J. Rissanen. Stochastic complexity and modeling. Annals of Statistics, 14(3):1080--1100, 1986.
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J. Rissanen, "Stochastic complexity and modeling," Annals of Statistics, vol. 14, pp. 1080--1100, Sept. 1986.
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J. Rissanen. Stochastic complexity and modeling. The Annals of Statistics, 14(3):1080-1100, 1986.
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J. Rissanen, \Stochastic complexity and modeling," Annals of Statistics, vol. 14, pp. 1080{ 1100, Sept. 1986.
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