Business Applications Of Neural Networks: The State-of-the-art Of Real-world ApplicationsBill Edisbury, Paulo J G Lisboa, Alfredo Vellido World Scientific, 2000. gada 30. aug. - 220 lappuses 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. |
No grāmatas satura
1.–5. rezultāts no 18.
viii. lappuse
... are characterised by arrays of highly interconnected cells, often arranged in layered structures, where each cell, or neuron, is roughly similar to the next. viii Business Applications of Neural Networks 2 What are neural networks?
... are characterised by arrays of highly interconnected cells, often arranged in layered structures, where each cell, or neuron, is roughly similar to the next. viii Business Applications of Neural Networks 2 What are neural networks?
xii. lappuse
... layers of adjustable weights', shown in figure 5. The cell layer sandwiched between the input nodes on the left, and the response nodes on the right, in figure 5, is called a hidden layer. The network weights are estimated using ...
... layers of adjustable weights', shown in figure 5. The cell layer sandwiched between the input nodes on the left, and the response nodes on the right, in figure 5, is called a hidden layer. The network weights are estimated using ...
xiii. lappuse
... layer Perceptron (MLP) neural network. The network inputs are labelled x, the hidden nodes by h and the predicted output by y. The remaining symbols indicate connection weights, w and v, and threshold values, b. There are alternative ...
... layer Perceptron (MLP) neural network. The network inputs are labelled x, the hidden nodes by h and the predicted output by y. The remaining symbols indicate connection weights, w and v, and threshold values, b. There are alternative ...
2. lappuse
... layer perceptron', or radial basis function networks” would be used. These methods extend naturally from the techniques of linear and logistic regression. These neural network architectures form a method of estimating posterior ...
... layer perceptron', or radial basis function networks” would be used. These methods extend naturally from the techniques of linear and logistic regression. These neural network architectures form a method of estimating posterior ...
3. lappuse
... layer, where the node with the “best" value is considered the “winner". Usually for a given numeric input record, the value concerned is the generalised “distance” between the input record and the weight values linking the input layer ...
... layer, where the node with the “best" value is considered the “winner". Usually for a given numeric input record, the value concerned is the generalised “distance” between the input record and the weight values linking the input layer ...
Saturs
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 |
Citi izdevumi - Skatīt visu
Business Applications of Neural Networks: The State-of-the-art of Real-world ... Paulo J. G. Lisboa,Bill Edisbury,Alfredo Vellido Ierobežota priekšskatīšana - 2000 |
Business Applications of Neural Networks: The State-of-the-art of Real-world ... Bill Edisbury Ierobežota priekšskatīšana - 2000 |
Business Applications of Neural Networks: The State-of-the-art of Real-world ... Paulo J. G. Lisboa,Bill Edisbury,Alfredo Vellido Priekšskatījums nav pieejams - 2000 |
Bieži izmantoti vārdi un frāzes
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