Regression with input-dependent noise: a Gaussian process treatment
NIPS '97 Proceedings of the 1997 conference on Advances in neural information processing systems 10
Gaussian Process Regression: Active Data Selection and Test Point Rejection
Mustererkennung 2000, 22. DAGM-Symposium
The Psychophysics of Temperature Perception and Thermal-Interface Design
HAPTICS '02 Proceedings of the 10th Symposium on Haptic Interfaces for Virtual Environment and Teleoperator Systems
Bayesian treed gaussian process models
Bayesian treed gaussian process models
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Most likely heteroscedastic Gaussian process regression
Proceedings of the 24th international conference on Machine learning
A new modelling methodology to control HVAC systems
Expert Systems with Applications: An International Journal
A framework for optimization under limited information
Proceedings of the 5th International ICST Conference on Performance Evaluation Methodologies and Tools
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This paper presents a study of user comfort levels using an ubiquitous interface. The aim is to analyse the comfort function of an individual as opposed to previous approaches that look at the average human being. The data is analysed using Gaussian Process regression which allows several mechanisms to be exploited. These include regression on the data to give an estimate of a users comfort function. The prediction variance is also estimated and outlier influence can be reduced easily. In addition, a natural means of combining the preferences of users falls out of the approach. The combination algorithm takes into account fairness tempered by the quality of the user' preference estimates. Empirical results show that the combined preferences have a well defined maxima which can be used as a control signal for a HVAC system. The Gaussian Process approach is hierarchical and interestingly, while those users studied have differing preferences, their hyperparameters (at the second level of the hierarchy) are concentrated; i.e. there is a strong commonality across individuals in this domain.