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» Kernels for Multi--task Learning
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ICML
2008
IEEE
14 years 8 months ago
The skew spectrum of graphs
The central issue in representing graphstructured data instances in learning algorithms is designing features which are invariant to permuting the numbering of the vertices. We pr...
Risi Imre Kondor, Karsten M. Borgwardt
COLT
2008
Springer
13 years 9 months ago
Linear Algorithms for Online Multitask Classification
We design and analyze interacting online algorithms for multitask classification that perform better than independent learners whenever the tasks are related in a certain sense. W...
Giovanni Cavallanti, Nicolò Cesa-Bianchi, C...
NIPS
2008
13 years 9 months ago
Regularized Policy Iteration
In this paper we consider approximate policy-iteration-based reinforcement learning algorithms. In order to implement a flexible function approximation scheme we propose the use o...
Amir Massoud Farahmand, Mohammad Ghavamzadeh, Csab...
ICML
2009
IEEE
14 years 8 months ago
Optimal reverse prediction: a unified perspective on supervised, unsupervised and semi-supervised learning
Training principles for unsupervised learning are often derived from motivations that appear to be independent of supervised learning. In this paper we present a simple unificatio...
Linli Xu, Martha White, Dale Schuurmans
AAAI
2008
13 years 10 months ago
Multi-View Local Learning
The idea of local learning, i.e., classifying a particular example based on its neighbors, has been successfully applied to many semi-supervised and clustering problems recently. ...
Dan Zhang, Fei Wang, Changshui Zhang, Tao Li