Publication Details
Title: Beyond Classification -- Large-Scale Gaussian Process Inference and Uncertainty Prediction
Author: A. Freytag, E. Rodner, P. Bodesheim, and J. Denzler
Bibliographic Information: Presented at the BigVision Workshop at the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nevada
Date: December 2012
Research Area: Vision
Type: Talk or presentation
PDF: http://www.icsi.berkeley.edu/pubs/vision/ICSI_BeyondClassification12.pdf
Overview:
Due to the massive (labeled) data available on the web, a tremendous interest in large-scale machine learning methods has emerged in the last years. Whereas, most of the work done in this new area of research focused on fast and efficient classification algorithms, we show in this paper how other aspects of learning can also be covered using massive datasets. The paper briefly presents techniques allowing for utilizing the full posterior obtained from Gaussian process regression (predictive mean and variance) with tens of thousands of data points and without relying on sparse approximation approaches. Experiments are done for active learning and one-class classification showing the benefits in large-scale settings.
Acknowledgements:
This work was partially funded by the Deutscher Akademischer Austausch Dienst (DAAD) through a postdoctoral fellowship.
Bibliographic Reference:
A. Freytag, E. Rodner, P. Bodesheim, and J. Denzler. Beyond Classification -- Large-Scale Gaussian Process Inference and Uncertainty Prediction. Presented at the BigVision Workshop at the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nevada, December 2012
Author: A. Freytag, E. Rodner, P. Bodesheim, and J. Denzler
Bibliographic Information: Presented at the BigVision Workshop at the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nevada
Date: December 2012
Research Area: Vision
Type: Talk or presentation
PDF: http://www.icsi.berkeley.edu/pubs/vision/ICSI_BeyondClassification12.pdf
Overview:
Due to the massive (labeled) data available on the web, a tremendous interest in large-scale machine learning methods has emerged in the last years. Whereas, most of the work done in this new area of research focused on fast and efficient classification algorithms, we show in this paper how other aspects of learning can also be covered using massive datasets. The paper briefly presents techniques allowing for utilizing the full posterior obtained from Gaussian process regression (predictive mean and variance) with tens of thousands of data points and without relying on sparse approximation approaches. Experiments are done for active learning and one-class classification showing the benefits in large-scale settings.
Acknowledgements:
This work was partially funded by the Deutscher Akademischer Austausch Dienst (DAAD) through a postdoctoral fellowship.
Bibliographic Reference:
A. Freytag, E. Rodner, P. Bodesheim, and J. Denzler. Beyond Classification -- Large-Scale Gaussian Process Inference and Uncertainty Prediction. Presented at the BigVision Workshop at the 26th Annual Conference on Neural Information Processing Systems (NIPS 2012), Lake Tahoe, Nevada, December 2012