576 search results - page 1 / 116 » Structured metric learning for high dimensional problems |
KDD
2008
ACM
14 years 5 months ago
2008
ACM
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
ECML
2006
Springer
13 years 8 months ago
2006
Springer
A critical problem in clustering research is the definition of a proper metric to measure distances between points. Semi-supervised clustering uses the information provided by the ...
UAI
2000
13 years 5 months ago
2000
This paper is about the use of metric data structures in high-dimensionalor non-Euclidean space to permit cached sufficientstatisticsaccelerationsof learning algorithms. It has re...
ICML
2008
IEEE
14 years 5 months ago
2008
IEEE
In this paper we perform an empirical evaluation of supervised learning on highdimensional data. We evaluate performance on three metrics: accuracy, AUC, and squared loss and stud...
CORR
2010
Springer
13 years 4 months ago
2010
Springer
The problem of learning forest-structured discrete graphical models from i.i.d. samples is considered. An algorithm based on pruning of the Chow-Liu tree through adaptive threshol...