Business Applications Of Neural Networks: The State-of-the-art Of Real-world ApplicationsBill Edisbury, Paulo J G Lisboa, Alfredo Vellido World Scientific, 30 авг. 2000 г. - Всего страниц: 220 Neural networks are increasingly being used in real-world business applications and, in some cases, such as fraud detection, they have already become the method of choice. Their use for risk assessment is also growing and they have been employed to visualise complex databases for marketing segmentation. This boom in applications covers a wide range of business interests — from finance management, through forecasting, to production. The combination of statistical, neural and fuzzy methods now enables direct quantitative studies to be carried out without the need for rocket-science expertise.This book reviews the state-of-the-art in current applications of neural-network methods in three important areas of business analysis. It includes a tutorial chapter to introduce new users to the potential and pitfalls of this new technology. |
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Стр. vii
Bill Edisbury, Paulo J G Lisboa, Alfredo Vellido. Business. Applications. of. Neural. Networks. P.J.G. Lisboa and A. Vellido School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool L33AF, UK E-mail: p.j. ...
Bill Edisbury, Paulo J G Lisboa, Alfredo Vellido. Business. Applications. of. Neural. Networks. P.J.G. Lisboa and A. Vellido School of Computing and Mathematical Sciences Liverpool John Moores University Liverpool L33AF, UK E-mail: p.j. ...
Стр. viii
... neural networks have found a large number of more routine applications in quantitative business analysis, from ... Artificial neural networks is a term used to describe a gross analogy with ... Neural Networks 2 What are neural networks?
... neural networks have found a large number of more routine applications in quantitative business analysis, from ... Artificial neural networks is a term used to describe a gross analogy with ... Neural Networks 2 What are neural networks?
Стр. ix
... a maximum firing frequency, denoted here as a unity response. X1 X3 Figure 1: Model of an artificial neural cell. The essential features of neural networks are the interconnection between cells through synapses whose strength is ...
... a maximum firing frequency, denoted here as a unity response. X1 X3 Figure 1: Model of an artificial neural cell. The essential features of neural networks are the interconnection between cells through synapses whose strength is ...
Стр. x
... a 2). ask * D.5P * 0.4}. * 0.3H. s 0.2}- . 0.1 H. o “A ST2 ...To To 2. TST 4 Figure 2: Typical response function for a single neuron. This curve has the functional form y = 1/[1+exp(-a)] ... a x3 = 1. x Business Applications of Neural Networks.
... a 2). ask * D.5P * 0.4}. * 0.3H. s 0.2}- . 0.1 H. o “A ST2 ...To To 2. TST 4 Figure 2: Typical response function for a single neuron. This curve has the functional form y = 1/[1+exp(-a)] ... a x3 = 1. x Business Applications of Neural Networks.
Стр. xii
... artificial neural networks must now be given a statistical perspective. This is because, in the real world, customer data from different classes of applicant do not usually separate neatly into distinct clusters. Rather, they have a ...
... artificial neural networks must now be given a statistical perspective. This is because, in the real world, customer data from different classes of applicant do not usually separate neatly into distinct clusters. Rather, they have a ...
Содержание
1 | |
Chapter 2 Extracting Rules Concerning Market Segmentation from Artificial Neural Networks | 13 |
Chapter 3 Characterising and Segmenting the BusinesstoConsumer ECommerce Market Using Neural Networks | 29 |
Chapter 4 A Neurofuzzy Model for Predicting Business Bankruptcy | 55 |
Chapter 5 Neural Networks for Analysis of Financial Statements | 73 |
Chapter 6 Developments in Accurate Consumer Risk Assessment Technology | 85 |
Chapter 7 Strategies for Exploiting Neural Networks in Retail Finance | 99 |
Chapter 8 Novel Techniques for Profiling and Fraud Detection in Mobile Telecommunications | 113 |
Chapter 9 Detecting Payment Card Fraud with Neural Networks | 141 |
Chapter 10 Money Laundering Detection with a NeuralNetwork | 159 |
Chapter 11 Utilising Fuzzy Logic and Neurofuzzy for Business Advantage | 173 |
Index | 195 |
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Business Applications of Neural Networks: The State-of-the-art of Real-world ... Bill Edisbury Ограниченный просмотр - 2000 |
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accuracy AFPR aircraft application Artificial Neural Networks behaviour British Airways card fraud cardholder classification clusters data mining data protection data set data space database decision engine described developed example extracted factors Falcon score Figure financial ratios fraud detection system fraudster fraudulent fuzzy logic fuzzy logic system fuzzyTECH Genetic Algorithms hidden unit activation identified input variables issuers layer linear linear discriminant analysis logistic regression Machine Learning methods mobile money laundering neural network model neurofuzzy nodes non-linear optimisation output parameters patterns performance perimetry personal data predictive model problem profiles prototype pruning pruning algorithm ratios rule block samples scorecard segmentation self-organising map Self-Organizing Map Shopping experience solution statistical subset techniques telecommunications threshold Toll Tickets tool transactions unsupervised learning variable selection vector visualization weights