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Titlebook: Neural Networks for Conditional Probability Estimation; Forecasting Beyond P Dirk Husmeier Book 1999 Springer-Verlag London Limited 1999 al

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书目名称Neural Networks for Conditional Probability Estimation
副标题Forecasting Beyond P
编辑Dirk Husmeier
视频video
概述Provides unique, comprehensive coverage of generalisation and regularisation: Provides the first real-world test results for recent theoretical findings on the generalisation performance of committees
丛书名称Perspectives in Neural Computing
图书封面Titlebook: Neural Networks for Conditional Probability Estimation; Forecasting Beyond P Dirk Husmeier Book 1999 Springer-Verlag London Limited 1999 al
描述Conventional applications of neural networks usually predict a single value as a function of given inputs. In forecasting, for example, a standard objective is to predict the future value of some entity of interest on the basis of a time series of past measurements or observations. Typical training schemes aim to minimise the sum of squared deviations between predicted and actual values (the ‘targets‘), by which, ideally, the network learns the conditional mean of the target given the input. If the underlying conditional distribution is Gaus­ sian or at least unimodal, this may be a satisfactory approach. However, for a multimodal distribution, the conditional mean does not capture the relevant features of the system, and the prediction performance will, in general, be very poor. This calls for a more powerful and sophisticated model, which can learn the whole conditional probability distribution. Chapter 1 demonstrates that even for a deterministic system and ‘be­ nign‘ Gaussian observational noise, the conditional distribution of a future observation, conditional on a set of past observations, can become strongly skewed and multimodal. In Chapter 2, a general neural network struc
出版日期Book 1999
关键词algorithms; dynamical systems; neural network; neural networks; noise; pattern; pattern recognition; traini
版次1
doihttps://doi.org/10.1007/978-1-4471-0847-4
isbn_softcover978-1-85233-095-8
isbn_ebook978-1-4471-0847-4Series ISSN 1431-6854
issn_series 1431-6854
copyrightSpringer-Verlag London Limited 1999
The information of publication is updating

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Dirk Husmeier PhDt this can actually only be the case at . = ., at any finite temperature the ions will be deflected from their equilibrium positions and at a given time . there will be no periodic potential anymore. The consequences of this are to be examined in this chapter.
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Dirk Husmeier PhDon in one dimension; Monte Carlo methods are also introduced and results are presented. Finally, band magnetism is discussed and the Stoner theory of band magnetism is derived from the Hubbard model in a mean field approximation. The giant magnetoresistance (GMR) effect is explained as a current app
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Dirk Husmeier PhDic field to matter in a perturbative way, Rabi oscillations and the optical Stark effect are treated. The semiconductor Bloch equations for the occupation probabilities and the polarization taking into account the Coulomb interaction between the electrons (or between the electrons in the conduction
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Dirk Husmeier PhD spin-orbit coupling on surface states is treated. In this context the class of the recently detected topological insulators, materials of significant importance for spin electronics, are discussed. Particular emphasis, hereby, is laid on the new type of topologically protected surface states with w
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Dirk Husmeier PhDelopment of a detailed understanding of the surface electronic structure. On the theoretical side, the general approach is similar to that for the bulk crystal: In essence the one-electron approximation is used and one tries to solve the Schrödinger equation for an electron near the surface. A varie
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Dirk Husmeier PhDms per cm. must be studied against the background of about 10. atoms present in a bulk volume of one cm.. In surface and interface physics the appropriate geometry for a scattering experiment is thus the reflection geometry. Furthermore, only particles that do not penetrate too deeply into the solid
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