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What is Error Function

Chapter 12
Management and Marketing for Improved Retail Competitiveness and Performance
Defines the measure of the difference between the output variables of the ANN, that is, the values produced by the ANN, and the values of the dependent variables in the sample.
Published in Chapter:
Artificial Neural Networks and Discrete Choice Models: Sales Forecast in Supermarket Products
Paulo Botelho Pires (CEOS, Polytechnic of Porto, Portugal) and José Duarte Santos (CEOS, ISCAP, Polytechnic of Porto, Portugal)
Copyright: © 2023 |Pages: 26
DOI: 10.4018/978-1-6684-8574-3.ch012
Abstract
The performance of artificial neural networks was compared with the performance of discrete choice models in predicting the purchase of products with weak involvement. A comprehensive literature review on the main paradigms of artificial neural networks was carried out, namely variants of the back-propagation algorithm, radial basis function, and genetic computing. Within the class of discrete choice models, the authors restricted the comparison to the multinomial logit model and the mixed logit. The performance of the models was measured in a database of grocery purchases in supermarkets. Artificial neural networks outperformed discrete choice models in predicting sales in supermarkets, and both types of models demonstrated strong predictive power. As a result, both can be reliably used in marketing to estimate individual or collective probabilities of supermarket product purchases.
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Chapter 20
Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images
The formula that defines the quantity that we wish to optimize. In our case, it is based on the intensity error and a stiffness constraint.
Published in Chapter: Groupwise Non-Rigid Image Alignment Using Few Parameters: Registration of Facial and Medical Images; From: Handbook of Research on Human-Computer Interfaces and New Modes of Interactivity
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Chapter 26
Stochastic Neural Network Classifiers
A mathematically differentiable function related to the difference between the desired output of the neural network and the actual output. During the learning phase the network minimizes the error function by dynamically changing the strength (weight) of the connections between the neurons in the network.
Published in Chapter: Stochastic Neural Network Classifiers; From: Encyclopedia of Information Science and Technology, Third Edition
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Chapter 577
Recurrent Neural Networks for Predicting Mobile Device State
A function used for assessing how well a machine learning method performs.
Published in Chapter: Recurrent Neural Networks for Predicting Mobile Device State; From: Encyclopedia of Information Science and Technology, Fourth Edition
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Chapter 525
Design of Compensators for Comb Decimation Filters
The difference of the desired and designed magnitude responses of compensated filter.
Published in Chapter: Design of Compensators for Comb Decimation Filters; From: Encyclopedia of Information Science and Technology, Fourth Edition
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