Machine Learning Methods for Predicting Failures in Hard Drives: A Multiple-Instance Application
The Journal of Machine Learning Research
Nonparametric factor analysis with beta process priors
ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Evaluating Learning Algorithms: A Classification Perspective
Evaluating Learning Algorithms: A Classification Perspective
The Indian Buffet Process: An Introduction and Review
The Journal of Machine Learning Research
The contextual focused topic model
Proceedings of the 18th ACM SIGKDD international conference on Knowledge discovery and data mining
A Bayesian non-parametric viewpoint to visual tracking
WACV '13 Proceedings of the 2013 IEEE Workshop on Applications of Computer Vision (WACV)
Machine learning for science and society
Machine Learning
Hi-index | 0.00 |
Prediction of water pipe condition through statistical modelling is an important element for the risk management strategy of water distribution systems. In this work a hierarchical nonparametric model has been used to enhance the performance of pipe condition assessment. The main aims of this work are three-fold: (1) For sparse incident data, develop an efficient approximate inference algorithm based on hierarchical beta process. (2) Apply the hierarchical beta process based method to water pipe condition assessment. (3) Interpret the outcomes in financial terms usable by the water utilities. The experimental results show superior performance of the proposed method compared to current best practice methods, leading to substantial savings on reactive repairs and maintenance, as well as improved prioritization for capital expenditure.