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Titlebook: Neural Information Processing: Research and Development; Jagath Chandana Rajapakse,Lipo Wang Book 2004 Springer-Verlag Berlin Heidelberg 2

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31#
發(fā)表于 2025-3-27 00:13:51 | 只看該作者
Adaptive Training for Combining Classifier Ensemblesrough their combining. This recent interest has been marked by the introduction of a variety of combining methods that improve the overall accuracy. However, it has been noted that for multiple classifier approaches to be useful its members should demonstrate error-independence. Ideally, in terms of
32#
發(fā)表于 2025-3-27 04:28:18 | 只看該作者
Combination Strategies for Finding Optimal Neural Network Architecture and Weightsgy combines evolutionary algorithms with direct/matrix solution methods such as Gram-Schmidt, singular value decomposition, etc., to achieve optimal weights for hidden and output layers. The proposed method uses evolutionary algorithms in the first layer and the least square method (LS) in the secon
33#
發(fā)表于 2025-3-27 07:49:40 | 只看該作者
Biologically inspired recognition system for car detection from real-time video streamslective attention. An object, in this application a car, is represented as a collection of features (horizontal and vertical edges) arranged at specific spatial locations with respect to the position of the fixation point (the central edge). The collection of conditional probabilities, that estimate
34#
發(fā)表于 2025-3-27 13:10:15 | 只看該作者
Financial Time Series Prediction Using Non-fixed and Asymmetrical Margin Setting with Momentum in Sueristics of small sample size, high noise and non-stationary. Especially the volatility of the time series is time-varying and embeds some valuable information about the series. Previously, we had proposed to use the volatility in the data to adaptively change the width of the margin in SVR. We have
35#
發(fā)表于 2025-3-27 14:45:29 | 只看該作者
A Method for Applying Neural Networks to Control of Nonlinear Systemses not use neural network directly as a nonlinear controller or nonlinear prediction model, but use it indirectly via an ARX-like macro-model, in which neural network is embedded. The ARX-like model incorporating neural network is constructed in such a way that it has similar linear properties to a
36#
發(fā)表于 2025-3-27 21:15:48 | 只看該作者
37#
發(fā)表于 2025-3-27 23:21:22 | 只看該作者
38#
發(fā)表于 2025-3-28 02:15:20 | 只看該作者
39#
發(fā)表于 2025-3-28 06:36:36 | 只看該作者
40#
發(fā)表于 2025-3-28 13:26:11 | 只看該作者
Alexander A. Kharlamov,Vladimir V. Raevskycritical role of soil in biogeochemical processes is tied to its properties and place—porous, structured, and spatially variable, it serves as a conduit, buffer, and transformer of water, solutes and gases. Yet what is complex, life-giving, and sacred to some, is ordinary, even ugly, to others. This
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