Principles of Neural Model Identification, Selection and Adequacy

Zapranis, Achilleas; Refenes, Apostolos-Paul N.

doi:10.1007/978-1-4471-0559-6

Cited by 62 publications

(68 citation statements)

References 0 publications

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“…Once, the parameters of the neural model (16) are estimated, we have to deal with the presence of flat minima (potentially many combinations of the network parameters corresponding to the same level if the empirical loss), especially if the statistical properties of the model are of importance, as it is the case in this complex financial application. In order to identify a locally unique solution, we have to remove all the irrelevant parameters, that is the parameters that do not affect the level of the empirical loss.…”

Section: Removing the Irrelevant Connectionsmentioning

confidence: 99%

Weather derivatives pricing: Modeling the seasonal residual variance of an Ornstein–Uhlenbeck temperature process with neural networks

Zapranis

Alexandridis

2009

Neurocomputing

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Section: Removing the Irrelevant Connectionsmentioning

confidence: 99%

Weather derivatives pricing: Modeling the seasonal residual variance of an Ornstein–Uhlenbeck temperature process with neural networks

Zapranis

Alexandridis

2009

Neurocomputing

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“…There is no information if X 2 should be removed from the model. In [24] a novel approach (parametric sampling) is presented in order to determine if a variable should be removed from the model. In parametric sampling new networks are created by bootstrapping the parameters of the initial network.…”

Section: Model Fitness and Sensitivity Criteriamentioning

confidence: 99%

“…In parametric sampling new networks are created by bootstrapping the parameters of the initial network. In order to reduce training times [24] use local bootstrap. Wavelets are local function and local bootstrapping may cannot be used.…”

Section: Model Fitness and Sensitivity Criteriamentioning

confidence: 99%

Artificial Intelligence Applications and Innovations III

Iliadis¹,

Maglogiann²,

Tsoumakasis³

et al. 2009

IFIP Advances in Information and Communication Technology

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-The scope of this study is to present a complete statistical framework for model identification of wavelet neural networks (WN). In each step in WN construction we test various methods already proposed in literature. In the first part we compare four different methods for the initialization and construction of the WN. Next various information criteria as well as sampling techniques proposed in previous works were compared in order to derive an algorithm for selecting the correct topology of a WN. Finally, In variable significance testing the performance of various sensitivity and model-fitness criteria were examined and an algorithm for selecting the significant explanatory variables is presented.

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“…The application of VC theory to them is quite well-advanced [34,35], but there are many other approaches, including ones based on statistical mechanics [36]. It is notoriously hard to understand why they make the predictions they do.…”

Section: Choice Of Architecturementioning

confidence: 99%

“…However, it is often impractical to settle on a good parametric form beforehand. In these cases, one must turn to nonparametric models, as discussed in §1.2.2; neural networks are a particular favorite here [35]. The so-called kernel smoothing methods are also particularly welldeveloped for time series, and often perform almost as well as parametric models [66].…”

Section: Nonlinear and Nonparametric Modelsmentioning

confidence: 99%

Methods and Techniques of Complex Systems Science: An Overview

Shalizi

Topics in Biomedical Engineering International Book Series

154

102

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In this chapter, I review the main methods and techniques of complex systems science. As a first step, I distinguish among the broad patterns which recur across complex systems, the topics complex systems science commonly studies, the tools employed, and the foundational science of complex systems. The focus of this chapter is overwhelmingly on the third heading, that of tools. These in turn divide, roughly, into tools for analyzing data, tools for constructing and evaluating models, and tools for measuring complexity. I discuss the principles of statistical learning and model selection; time series analysis; cellular automata; agent-based models; the evaluation of complex-systems models; information theory; and ways of measuring complexity. Throughout, I give only rough outlines of techniques, so that readers, confronted with new problems, will have a sense of which ones might be suitable, and which ones definitely are not.

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Principles of Neural Model Identification, Selection and Adequacy

Cited by 62 publications

References 0 publications

Weather derivatives pricing: Modeling the seasonal residual variance of an Ornstein–Uhlenbeck temperature process with neural networks

Weather derivatives pricing: Modeling the seasonal residual variance of an Ornstein–Uhlenbeck temperature process with neural networks

Artificial Intelligence Applications and Innovations III

Methods and Techniques of Complex Systems Science: An Overview

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