65 search results - page 4 / 13 » Is Nonparametric Learning Practical in Very High Dimensional... |
UAI
2000
13 years 8 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...
SIGIR
2009
ACM
14 years 2 months ago
2009
ACM
With the sheer growth of online user data, it becomes challenging to develop preference learning algorithms that are sufficiently flexible in modeling but also affordable in com...
NN
2010
Springer
13 years 5 months ago
2010
Springer
The ratio of two probability density functions is becoming a quantity of interest these days in the machine learning and data mining communities since it can be used for various d...
CVPR
2004
IEEE
14 years 9 months ago
2004
IEEE
Classifying high-dimensional numerical data is a very challenging problem. In high dimensional feature spaces, the performance of supervised learning methods suffer from the curse...
ICML
2007
IEEE
14 years 8 months ago
2007
IEEE
Supervised learning is difficult with high dimensional input spaces and very small training sets, but accurate classification may be possible if the data lie on a low-dimensional ...