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» On Kernel Methods for Relational Learning
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ICML
2004
IEEE
15 years 10 months ago
Using relative novelty to identify useful temporal abstractions in reinforcement learning
lative Novelty to Identify Useful Temporal Abstractions in Reinforcement Learning ?Ozg?ur S?im?sek ozgur@cs.umass.edu Andrew G. Barto barto@cs.umass.edu Department of Computer Scie...
Özgür Simsek, Andrew G. Barto
ESANN
2006
14 years 11 months ago
Variants of Unsupervised Kernel Regression: General cost functions
We present an extension to a recent method for learning of nonlinear manifolds, which allows to incorporate general cost functions. We focus on the -insensitive loss and visually d...
Stefan Klanke, Helge Ritter
AAAI
2007
15 years 4 days ago
Kernel Regression with Order Preferences
We propose a novel kernel regression algorithm which takes into account order preferences on unlabeled data. Such preferences have the form that point x1 has a larger target value...
Xiaojin Zhu, Andrew B. Goldberg
CVPR
2009
IEEE
16 years 5 months ago
Shared Kernel Information Embedding for Discriminative Inference
Latent Variable Models (LVM), like the Shared-GPLVM and the Spectral Latent Variable Model, help mitigate over- fitting when learning discriminative methods from small or modera...
David J. Fleet, Leonid Sigal, Roland Memisevic
IJCAI
2007
14 years 11 months ago
Parametric Kernels for Sequence Data Analysis
A key challenge in applying kernel-based methods for discriminative learning is to identify a suitable kernel given a problem domain. Many methods instead transform the input data...
Young-In Shin, Donald S. Fussell