Multiresolution Kernel Approximation for Gaussian Process Regression
| 
45 | 
50 | 
0
Abstract
Gaussian process regression generally does not scale to beyond a few thousands data points without applying some sort of kernel approximation method. Most approximations focus on the high eigenvalue part of the spectrum of the kernel matrix, K, which leads to bad performance when the length scale of the kernel is small. In this paper we introduce Multiresolution Kernel Approximation (MKA), the first true broad bandwidth kernel approximation algorithm. Important points about MKA are that it is memory efficient, and it is a direct method, which means that it also makes it easy to approximate 
and det(K).
还没有评论，说两句吧！
热门资源
Learning to learn...
The move from hand-designed features to learned...
A Mathematical Mo...
Direct democracy, where each voter casts one vo...
Stratified Strate...
In this paper we introduce Stratified Strategy ...
Rating-Boosted La...
The performance of a recommendation system reli...
Hierarchical Task...
We extend hierarchical task network planning wi...
智能在线
400-630-6780
聆听.建议反馈
E-mail: support@tusaishared.com
资源分类
