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Probabilistic Models for Preference Learning
Published on Jan 24, 20126009 Views
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Chapter list
Probabilistic Models for Preference Learning00:00
What is preference learning?00:34
Instance Preference01:13
Preference Functions03:49
Learning Preference Functions04:49
Gaussian Processes (a mini tutorial)06:49
Gaussian process covariance functions (kernels)07:53
Gaussian Processes for Nonlinear regression08:45
Gaussian process covariance functions09:00
Samples from GPs with di erent K (x, x')09:51
GP learning the kernel10:06
Using Gaussian Processes for Classi cation11:21
Support Vector Machines11:26
Support Vector Machines and Gaussian Processes12:31
A picture14:39
Matlab Demo: Gaussian Process Classi cation16:26
Some Comparisons16:29
Gaussian Processes for Preference Learning16:37
MAP Inference and Laplace Approximation19:25
Prediction21:25
Results22:12
Semi-supervised and Active Preference Learning - 123:49
Semi-supervised and Active Preference Learning - 229:45
Unsupervised Preference Learning: Choice Models34:00
Clustering Users Based on Preferences - 139:00
Clustering Users Based on Preference - 242:51
Summary43:12