Machine Learning
Neural Networks for Pattern Recognition
Neural Networks for Pattern Recognition
Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions
Neural Networks for Conditional Probability Estimation: Forecasting beyond Point Predictions
Improving Regressors using Boosting Techniques
ICML '97 Proceedings of the Fourteenth International Conference on Machine Learning
Conditional Density Estimation with Class Probability Estimators
ACML '09 Proceedings of the 1st Asian Conference on Machine Learning: Advances in Machine Learning
Distribution rules with numeric attributes of interest
PKDD'06 Proceedings of the 10th European conference on Principle and Practice of Knowledge Discovery in Databases
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There is a range of potential applications of Machine Learning where it would be more useful to predict the probability distribution for a variable rather than simply the most likely value for that variable. In meteorology and in finance it is often important to know the probability of a variable falling within (or outside) different ranges. In this paper we consider the prediction of surf height with the objective of predicting if it will fall within a given 'surfable' range. Prediction problems such as this are considerably more difficult if the distribution of the phenomenon is significantly different from a normal distribution. This is the case with the surf data we have studied. To address this we use an ensemble of mixture density networks to predict the probability density function. Our evaluation shows that this is an effective solution. We also describe a web-based application that presents these predictions in a usable manner.