نتایج جستجو برای: the following regularization parameter selection methods
تعداد نتایج: 16288343 فیلتر نتایج به سال:
power transformers are important equipments in power systems. thus there is a large number of researches devoted of power transformers. however, there is still a demand for future investigations, especially in the field of diagnosis of transformer failures. in order to fulfill the demand, the first part reports a study case in which four main types of failures on the active part are investigate...
in the area of vocabulary teaching and learning although much research has been done, only some of it has led to effective techniques of vocabulary teaching and many language learners still have problem learning vocabulary. the urge behind this study was to investigate three methods of teaching words. the first one was teaching words in context based on a traditional method of teaching that is,...
in chapter one we will describe definitions and preliminary results to provide the global context of our own results to be presented in detail in the subsequent chapters in chapter two we consider degree-one maps of the circle and we study their rotation set. our main result in this chapter says that if the map is topologically mixing then its rotation interval is nontrivial (that is, not reduc...
since its introduction in 1959, translation quality assessment (tqa) has been among the most addressed research topics in translation studies. during recent years, there has been a crucial increase on the study of tqa. various methods have come on scene. although these methods are based on scientific theories, most of them have remained at the level of theory. juliane house’s model is among tho...
Recently proposed l1-regularized maximum-likelihood optimization methods for learning sparse Markov networks result into convex problems that can be solved optimally and efficiently. However, the accuracy of such methods can be very sensitive to the choice of regularization parameter, and optimal selection of this parameter remains an open problem. Herein, we propose a maximum a posteriori prob...
In this paper, we propose a novel robust and pragmatic feature selection approach. Unlike those sparse learning based feature selection methods which tackle the approximate problem by imposing sparsity regularization in the objective function, the proposed method only has one `2,1-norm loss term with an explicit `2,0-Norm equality constraint. An efficient algorithm based on augmented Lagrangian...
in this thesis our aim is to construct vector field in r3 for which the corresponding one-dimensional maps have certain discontinuities. two kinds of vector fields are considered, the first the lorenz vector field, and the second originally introced here. the latter have chaotic behavior and motivate a class of one-parameter families of maps which have positive lyapunov exponents for an open in...
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