2012/10/06 by Shrihari Vasudevan, Vasudevan, Shrihari, Arman Melkumyan +3
Computer Science · Mathematics · #Artificial Intelligence (cs.AI) #FOS: Computer and information sciences #Gaussian Processes and Bayesian Inference #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Neural Networks and Applications #Target Tracking and Data Fusion in Sensor Networks #cs.AI #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1210.1928
53 pages, 33 figures; improved presentation
openalex publication_date 2012/10/06 · arxiv created 2013/09/05 · arxiv updated 2013/09/06 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
This paper evaluates heterogeneous information fusion using multi-task Gaussian processes in the context of geological resource modeling. Specifically, it empirically demonstrates that information integration across heterogeneous information sources leads to superior estimates of all the quantities being modeled, compared to modeling them individually. Multi-task Gaussian processes provide a powerful approach for simultaneous modeling of multiple quantities of interest while taking correlations between these quantities into consideration. Experiments are performed on large scale real sensor data.