Fedorov, V. V. 1972. Theory of optimal experiment. Academic Press.  Home/Search Document Not in Database Summary Related Articles Check |
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Exploring the Predictable - Schmidhuber (2002) (Correct)
.... curious, inquisitive machine learning systems designed to explore a given environment, and how this di ers from what we intuitively might want. Most previous work on exploring unknown data sets has focused on selecting single training exemplars maximizing traditional information gain [5,10,16,18,4]. Here typically the concept of a surprise is de ned in Shannon s sense [38] some event s surprise value or information content is the negative logarithm of its probability. This inspired simple reinforcement learning approaches to pure exploration [24,23,40] that use adaptive predictors to ....
V. V. Fedorov. Theory of optimal experiments. Academic Press, 1972.
Release from Active Learning/Model Selection Dilemma.. - Sugiyama, Ogawa (2002) (Correct)
....this problem active learning with model selection. Definition 1: Active learning with model selection) Determine sample points and select a model from a so that the generalization error JG is minimized: min , S#M JG [X , S] 2) In general, the model should be fixed for active learning [4, 7, 3, 6, 5, 14, 15, 18] , and conversely the training examples gathered at fixed sample points are required for model selection [8, 1, 13, 12, 11, 2, 16, 17] This implies that the problem of active learning with model selection can not be generally solved by simply combining existing active learning and model ....
V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
Bayesian Exploration for Mobile Robots - Sim (2000) (Correct)
....they are heuristics no consideration is given to the quality of the results, and the results themselves are impossible to validate or evaluate. This problem has been addressed by several authors in the context of Tarantola s inverse problem theory and Fedorov s theory of optimal experiments [45, 15]. MacKay s series of papers addresses the problem of Bayesian interpolation interpolating a function from sample observations in the presence of observational uncertainty [26, 27] MacKay exploits Shannon s entropy [36] to show that the optimal place from which to obtain the next sample is that ....
Fedorov. Theory of optimal experiments. Academic Press, 1972.
Acceleration Of D-Optimum Design Algorithms By Removing.. - Pronzato (2002) (Correct)
....1. Introduction For a compact space of IR , we consider generalized designs # that are probability measures on Y, and denote #(Y) the set of such designs. For any # #(Y) with #(dx) 1, we denote (1) M(#) xx # #(dx) A D optimum design problem (approximate theory) see, e.g. [1], corresponds to the determination of a so called D optimum design measure # # in #(Y) that maximises #(M) log det M(#) While original algorithms are of the vertex direction type, see [2, 1] faster convergence is obtained with vertex exchange, see [3, 4, 5] Define the directional (Frechet) ....
.... #(dx) 1, we denote (1) M(#) xx # #(dx) A D optimum design problem (approximate theory) see, e.g. 1] corresponds to the determination of a so called D optimum design measure # # in #(Y) that maximises #(M) log det M(#) While original algorithms are of the vertex direction type, see [2, 1], faster convergence is obtained with vertex exchange, see [3, 4, 5] Define the directional (Frechet) derivative at M 1 in the direction of M 2 , M 1 , M 2 ) lim ##0 #[ 1 #)M 1 #M 2 ] #(M 1 ) #. It satisfies F# (# 1 ; # 2 ) M(# 1 ) M(# 2 ) F# (# 1 , x)# 2 (dx) for any # ....
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V.V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
Learning Functions from Imperfect Positive Data - Zelezny (2001) (1 citation) (Correct)
....techniques. In the next section we shall see how to optimally (in the Bayes sense) learn functions with unknown domains and normally distributed noise in the output arguments. The outstanding role of the normal noise distribution has been extensively justified in many sources (see e.g. [2]) namely on the basis of the central limit theorem. We implemented the method in the ILP system Aleph. Section 3 describes an e#ective outlier identification technique applicable in the clause by clause theory construction performed by this system, modified as to follow the guideline developed in ....
V. V. Fedorov. Theory of optimal experiments. Academic Press, 1972.
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Fedorov, V. V. 1972. Theory of optimal experiment. Academic Press.
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V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
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V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
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Fedorov, V., Theory of optimal experiments, Acad. press, NY, 1972.
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V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
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Fedorov, V. V. 1972 Theory of optimal experiments. Academic.
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V. V. Fedorov. Theory of Optimal Experiments. Academic Press, New York, 1972.
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V.V. Fedorov, Theory of Optimal Experiments, Academic Press, New York, 1972.
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V.V. Fedorov, Theory of Optimal Experiments, Academic Press, New York, 1972.
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V. V. Fedorov, Theory of Optimal Experiments, Academic Press, 1972
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V.V. Fedorov. Theory of Optimal experiments. Academic Press, New York, 1972.
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V.V. Fedorov. Theory of Optimal Experiments. Academic Press, 1972.
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V.V. Fedorov, Theory of Optimal Experiments, Academic Press, New York, 1972.
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V. V. Fedorov, Theory of Optimal Experiments, Academic Press, New York, 1972.
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Fedorov, V.V., 1972, Theory of optimal experiments (Academic Press, New York).
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