PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Large-scale multiclass transduction
Thomas Gaertner, quoc le, Simon Burton, Alex Smola and S V N Vishwanathan
In: NIPS 2005, December 2005, Vancouver.


We present a method for performing transductive inference on very large datasets. Our algorithm is based on multiclass Gaussian processes and is effective whenever the multiplication of the kernel matrix or its inverse with a vector can be performed sufficiently fast. This holds, for instance, for certain graph and string kernels. Transduction is achieved by varia- tional inference over the unlabeled data subject to a balancing constraint.

PDF - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:2058
Deposited By:Alex Smola
Deposited On:16 January 2006