Machine Learning
Sparse bayesian learning and the relevance vector machine
The Journal of Machine Learning Research
Context-based vision system for place and object recognition
ICCV '03 Proceedings of the Ninth IEEE International Conference on Computer Vision - Volume 2
Machine Learning
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning)
A Unifying View of Sparse Approximate Gaussian Process Regression
The Journal of Machine Learning Research
LIBLINEAR: A Library for Large Linear Classification
The Journal of Machine Learning Research
Bagging for Gaussian process regression
Neurocomputing
Global Context Extraction for Object Recognition Using a Combination of Range and Visual Features
Dyn3D '09 Proceedings of the DAGM 2009 Workshop on Dynamic 3D Imaging
Gaussian Processes for Object Categorization
International Journal of Computer Vision
Tree Decomposition for Large-Scale SVM Problems
The Journal of Machine Learning Research
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Gaussian processes are powerful modeling tools in machine learning which offer wide applicability for regression and classification tasks due to their non-parametric and non-linear behavior. However, one of their main drawbacks is the training time complexity which scales cubically with the number of examples. Our work addresses this issue by combining Gaussian processes with random decision forests to enable fast learning. An important advantage of our method is its simplicity and the ability to directly control the tradeoff between classification performance and computational speed. Experiments on an indoor place recognition task and on standard machine learning benchmarks show that our method can handle large training sets of up to three million examples in reasonable time while retaining good classification accuracy.