Gradient method

Xem 1-20 trên 30 kết quả Gradient method
  • Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Modified nonlinear conjugate gradient method with sufficient descent condition for unconstrained optimization

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  • The paper presents a systematic approach to find optimal maintenance strategies for pavement management systems by using the World Bank’s Highway Development and Management System (HDM-4) combined with gradient methods proposed by Tsunokawa et al (2005). The optimal maintenance strategy is defined as the set of optimal maintenance options corresponding to different traffic levels and various initial pavement conditions, which minimizes the sum of agency costs and road user costs in present value or maximizes the net benefit to society over an analysis period.

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  • What-if models of pavement management analysis such as RTIM, HERS and HDM-4 predict the consequences of different maintenance options to be tested that are specified exogenously. Therefore, although they are often used to “optimize” maintenance options, they are not optimizing them in its true sense; they are merely used to find the best options among those tested. Since there are usually infinite numbers of options, it is impossible to exhaust all of them and only suboptimal optimizers are found.

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  • Although the rediscovery in the mid 1980s of the backpropagation algorithm by Rumelhart, Hinton, and Williams [1] has long been viewed as a landmark event in the history of neural network computing and has led to a sustained resurgence of activity, the relative ineffectiveness of this simple gradient method has motivated many researchers to develop enhanced training procedures. In fact, the neural network literature has been inundated with papers proposing alternative training Kalman Filtering and Neural Networks...

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  • After opening a new class of chemical reactions in 1964, reactions in which unpaired electrons of stable radicals were not involved, nitroxide (aminoxyl) radicals became one of the most interesting and rapidly developing area of modern physical chemistry with their application to biophysics, molecular biology, polymer sciences and medicine. Further development of this field depends on new pathways in the nitroxide chemistry, modern methods in EPR spectroscopy and revealing new perspective practical approaches....

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  • The main task in the independent component analysis (ICA) problem, formulated in Chapter 1, is to estimate a separating matrix that will give us the independent components. It also became clear that cannot generally be solved in closed form, that is, we cannot write it as some function of the sample or training set, whose value could be directly evaluated. Instead, the solution method is based on cost functions, also called objective functions or contrast functions. Solutions to ICA are found at the minima or maxima of these functions....

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  • PARAMETER-BASED KALMAN FILTER TRAINING: THEORY AND IMPLEMENTATION Gintaras V. Puskorius and Lee A. Feldkamp Ford Research Laboratory, Ford Motor Company, Dearborn, Michigan, U.S.A. (, 2.

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  • 422 METHOD DEVELOPMENT Figure 8-47. Resolution map for all critical pairs. Color scale on the left indicates the minimum resolution that is predicted for a particular color in the resolution map. The x axis is the gradient time and the y axis is the temperature. The crosshair can be moved to obtained the predicted conditions for optimal resolution of all critical pairs. See color plate.

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  • Using the gradient method, element concentrations within the lichen are usually observed to increase as the distance to the suspected source decreases. Gough and Erdman (1977) used linear regression to evaluate the relationship between distance from a coal fired power plant and metal levels in Xanthoparmelia chlorochroa. However, as Puckett (1988) points out, concentrations of many elements will not reach zero at large distances from pollution sources because they have essential nutritional roles or are normal components of the lichen when growing in its natural environment.

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  • CHAPTER 69 Binary Choice Models. 69.1. Fisher’s Scoring and Iteratively Reweighted Least Squares This section draws on chapter 55 about Numerical Minimization. Another important “natural” choice for the positive definite matrix Ri in the gradient method is available if one maximizes a likelihood function

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  • At a meeting in Moscow in June 2005, Gil Strang suggested that there be a collection of Gene Golub's work to highlight his many important contributions to numerical analysis. The three of us were honored to undertake this pleasant task, with publication timed for February "29", 2007, the 75th anniversary of Gene's birth. Gene chose 21 papers to include here, and we are grateful to the publishers for permission to reprint these works. We asked each of the coauthors to write about how the paper came to be written.

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  • Tuyển tập các báo cáo nghiên cứu về y học được đăng trên tạp chí y học Critical Care giúp cho các bạn có thêm kiến thức về ngành y học đề tài: Immobilized pH gradient-driven paper-based IEF: a new method for fractionating complex peptide mixtures before MS analysis...

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  • The purpose of this volume is to give some guidelines for the student concerning the solution of problems in the theory of Functions in Several Variables. The intension is not to write a textbook, but instead to give some hints of how to solve problems in this fi eld. It therefore cannot replace any given textbook, but it may be used as a supplement to such a book on Functions in Several Variables.

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  • A prototypical problem road agencies are faced with is to find the optimal application schedule of maintenance works for a given road section. To solve such problems what-if models such as the road transport investment model (RTIM), the highway economic requirements system (HERS), and the highway development and management tool (HDM-4) are widely used to predict the consequences of different maintenance options.

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  • Many machine learning problems can be cast as optimization problems. This lecture introduces optimization. The objective is for you to learn: The definitions of gradient and Hessian; the gradient descent algorithm; Newton’s algorithm; stochastic gradient descent (SGD) for online learning; popular variants, such as AdaGrad and Asynchronous SGD;...

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  • This lecture describes the construction of binary classifiers using a technique called Logistic Regression. The objective is for you to learn: How to apply logistic regression to discriminate between two classes; how to formulate the logistic regression likelihood; how to derive the gradient and Hessian of logistic regression; how to incorporate the gradient vector and Hessian matrix into Newton’s optimization algorithm so as to come up with an algorithm for logistic regression, which we call IRLS.

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  • This lecture introduces you sequence models. The goal is for you to learn about: Recurrent neural networks, the vanishing and exploding gradients problem, long-short term memory (LSTM) networks, applications of LSTM networks.

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  • This paper uses sequential stochastic dominance procedures to compare the joint distribution of health and income across space and time. It is the first application of which we are aware of methods to compare multidimensional distributions of income and health using procedures that are robust to aggregation techniques. The paper’s approach is more general than comparisons of health gradients and does not require the estimation of health equivalent incomes. We illustrate the approach by contrasting Canada and the US using comparable data.

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  • The Graphics Class Using Bitmap Fills and Lines In addition to applying gradients to fills and lines, you can use bitmaps to decorate your drawing’s fills and lines. Both the beginBitmapFill() and lineBitmapStyle() methods we cover in this section use instances of the BitmapData class. This class handles pixel color and alpha data and allows low-level manipulation of bitmaps. Conveniently, BitmapData is also the data type of bitmaps instantiated from the Flash Professional library using a linkage class.

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  • Assume that gi (x) = 1 (hence gk (x) = 0, k = i), update the expert i based on output error. Update gating network so that gi (x) is even closer to unity. Alternatively, a batch training method can be adopted: 1. Apply a clustering algorithm to cluster the set of training samples into n clusters. Use the membership information to train the gating network. 2. Assign each cluster to an expert module and train the corresponding expert module. 3. Fine-tune the performance using gradient-based learning. Note that the function of the gating network is to partition the feature...

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