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» New Regularized Algorithms for Transductive Learning
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PKDD
2009
Springer
117views Data Mining» more  PKDD 2009»
13 years 11 months ago
New Regularized Algorithms for Transductive Learning
Abstract. We propose a new graph-based label propagation algorithm for transductive learning. Each example is associated with a vertex in an undirected graph and a weighted edge be...
Partha Pratim Talukdar, Koby Crammer
JMLR
2006
186views more  JMLR 2006»
13 years 4 months ago
Manifold Regularization: A Geometric Framework for Learning from Labeled and Unlabeled Examples
We propose a family of learning algorithms based on a new form of regularization that allows us to exploit the geometry of the marginal distribution. We focus on a semi-supervised...
Mikhail Belkin, Partha Niyogi, Vikas Sindhwani
COLT
2007
Springer
13 years 11 months ago
Transductive Rademacher Complexity and Its Applications
We present data-dependent error bounds for transductive learning based on transductive Rademacher complexity. For specific algorithms we provide bounds on their Rademacher complex...
Ran El-Yaniv, Dmitry Pechyony
ICML
2003
IEEE
14 years 5 months ago
Transductive Learning via Spectral Graph Partitioning
We present a new method for transductive learning, which can be seen as a transductive version of the k nearest-neighbor classifier. Unlike for many other transductive learning me...
Thorsten Joachims
COLT
2006
Springer
13 years 8 months ago
Stable Transductive Learning
Abstract. We develop a new error bound for transductive learning algorithms. The slack term in the new bound is a function of a relaxed notion of transductive stability, which meas...
Ran El-Yaniv, Dmitry Pechyony