| 書目名稱 | Neural Networks for Conditional Probability Estimation | | 副標(biāo)題 | Forecasting Beyond P | | 編輯 | Dirk Husmeier | | 視頻video | http://file.papertrans.cn/664/663714/663714.mp4 | | 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 | | 叢書名稱 | Perspectives in Neural Computing | | 圖書封面 |  | | 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 | | 關(guān)鍵詞 | algorithms; dynamical systems; neural network; neural networks; noise; pattern; pattern recognition; traini | | 版次 | 1 | | doi | https://doi.org/10.1007/978-1-4471-0847-4 | | isbn_softcover | 978-1-85233-095-8 | | isbn_ebook | 978-1-4471-0847-4Series ISSN 1431-6854 | | issn_series | 1431-6854 | | copyright | Springer-Verlag London Limited 1999 |
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