Sciweavers

1364 search results - page 68 / 273
» Sampling Methods for Unsupervised Learning
Sort
View
COLT
2005
Springer
15 years 8 months ago
Towards a Theoretical Foundation for Laplacian-Based Manifold Methods
In recent years manifold methods have attracted a considerable amount of attention in machine learning. However most algorithms in that class may be termed “manifold-motivatedâ€...
Mikhail Belkin, Partha Niyogi
ICML
2006
IEEE
16 years 3 months ago
Kernelizing the output of tree-based methods
We extend tree-based methods to the prediction of structured outputs using a kernelization of the algorithm that allows one to grow trees as soon as a kernel can be defined on the...
Florence d'Alché-Buc, Louis Wehenkel, Pierr...
123
Voted
PR
2008
144views more  PR 2008»
15 years 2 months ago
Kernel quadratic discriminant analysis for small sample size problem
It is generally believed that quadratic discriminant analysis (QDA) can better fit the data in practical pattern recognition applications compared to linear discriminant analysis ...
Jie Wang, Konstantinos N. Plataniotis, Juwei Lu, A...
AAAI
2008
15 years 4 months ago
Adaptive Importance Sampling with Automatic Model Selection in Value Function Approximation
Off-policy reinforcement learning is aimed at efficiently reusing data samples gathered in the past, which is an essential problem for physically grounded AI as experiments are us...
Hirotaka Hachiya, Takayuki Akiyama, Masashi Sugiya...
98
Voted
ICML
2009
IEEE
16 years 3 months ago
On sampling-based approximate spectral decomposition
This paper addresses the problem of approximate singular value decomposition of large dense matrices that arises naturally in many machine learning applications. We discuss two re...
Sanjiv Kumar, Mehryar Mohri, Ameet Talwalkar