You might also like

More about Neural Networks for Conditional Probability Estimation

• Book details
• Synopsis
• Trending among others

You get 125 loyalty points

Book synopsis

This volume presents a neural network architecture for the prediction of conditional probability densities - which is vital when carrying out universal approximation on variables which are either strongly skewed or multimodal. Two alternative approaches are discussed: the GM network, in which all parameters are adapted in the training scheme, and the GM-RVFL model which draws on the random functional link net approach. Points of particular interest are: - it examines the modification to standard approaches needed for conditional probability prediction; - it provides the first real-world test results for recent theoretical findings about the relationship between generalisation performance of committees and the over-flexibility of their members; This volume will be of interest to all researchers, practitioners and postgraduate / advanced undergraduate students working on applications of neural networks - especially those related to finance and pattern recognition.

Book details

Book category Books in English Society & social sciences Society & culture: general Social issues & processes

• Full title: Neural Networks for Conditional Probability Estimation
• Subtitle: Forecasting Beyond Point Predictions
• Author: Dirk Husmeier
• Language: English
• Binding: Paperback
• Number of pages: 275
• EAN: 9781852330958
• ISBN: 1852330953
• ID: 02181954
• Publisher: Springer London Ltd
• Weight: 462 g
• Dimensions: 235 × 155 × 17 mm
• Date of publishing: 22. February 1999

Trending among others

---

---

---