J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN 2000. Home/Search Document Not in Database Summary Related Articles Check |
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Pareto Evolutionary Neural Networks - Fieldsend, Singh (2003) (Correct)
....than just minimising the forecast Euclidean error. Recent work that attempts to encapsulate multiple objectives using NNs have introduced augmentations to the traditional approaches of NN training. These have been in the form of propagating a linear sum of errors [50, 51] a product of terms [54], and penalising particular mis classifications more heavily [44] However these approaches implicitly assume the practitioner has some knowledge of the true Pareto error front defined by the generating process, and the features and NN topology they are using to model it (in order to specify the ....
....be needed with different weights (a) Equation 1) to obtain N different individuals on the Pareto front (therefore it would be subject to high computation time) However, where the true Pareto front itself is convex even this expensive option is infeasible. An example of this draw back is shown in [54] where a composite error term is used. 54] reports that the composite error weights were adjusted a number of times in order to find the best results on the test data, underlining the fact that the shape of the true Pareto error front was unknown. A general framework for training NNs which is ....
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J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN 2000.
Pareto Multi-Objective Non-Linear Regression Modelling to.. - Fieldsend, Singh (2002) (Correct)
....than just minimising the forecast Euclidean error. In order to encapsulate multiple objectives, recent approaches to time series forecasting using NNs have introduced augmentations to traditional learning algorithms. These have been in the form of propagating a linear sum of errors [8] 9] [10], and penalising particular mis classifications more heavily [11] However these approaches implicitly assume the practitioner has some knowledge of the true Pareto error front defined by the generating process, and the features and network topology they are using to model it. A Pareto error ....
J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN
Pareto Multi-Objective Non-Linear Regression - Modelling To Aid (Correct)
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J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN 2000.
Pareto Evolutionary Neural Networks - Jonathan Fieldsend Member (Correct)
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J. Yao and C. Tan, "Time dependant Directional Profit Model for Financial Time Series Forecasting," in IJCNN 2000.
Pareto Multi-Objective Non-Linear Regression Modelling to .. - Jonathan Fieldsend And (2002) (Correct)
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J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN 2000.
Pareto Multi-Objective Non-Linear Regression Modelling to .. - Jonathan Fieldsend And (2002) (Correct)
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J. Yao and C.L. Tan. Time dependant Directional Profit Model for Financial Time Series Forecasting. In IJCNN 2000.
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