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» Approximation Methods for Supervised Learning
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TSMC
2010
13 years 5 months ago
Distance Approximating Dimension Reduction of Riemannian Manifolds
We study the problem of projecting high-dimensional tensor data on an unspecified Riemannian manifold onto some lower dimensional subspace1 without much distorting the pairwise geo...
Changyou Chen, Junping Zhang, Rudolf Fleischer
ICML
2005
IEEE
14 years 12 months ago
Compact approximations to Bayesian predictive distributions
We provide a general framework for learning precise, compact, and fast representations of the Bayesian predictive distribution for a model. This framework is based on minimizing t...
Edward Snelson, Zoubin Ghahramani
TREC
2007
14 years 10 days ago
Relaxed Online SVMs in the TREC Spam Filtering Track
Relaxed Online Support Vector Machines (ROSVMs) have recently been proposed as an efficient methodology for attaining an approximate SVM solution for streaming data such as the on...
David Sculley, Gabriel Wachman
ICML
1999
IEEE
14 years 12 months ago
Least-Squares Temporal Difference Learning
Excerpted from: Boyan, Justin. Learning Evaluation Functions for Global Optimization. Ph.D. thesis, Carnegie Mellon University, August 1998. (Available as Technical Report CMU-CS-...
Justin A. Boyan
ILP
2004
Springer
14 years 4 months ago
Learning an Approximation to Inductive Logic Programming Clause Evaluation
One challenge faced by many Inductive Logic Programming (ILP) systems is poor scalability to problems with large search spaces and many examples. Randomized search methods such as ...
Frank DiMaio, Jude W. Shavlik