| | |
| | |
Stat |
Members: 3197 Articles: 2'218'097 Articles rated: 2592
27 June 2022 |
|
| | | |
|
Article overview
| |
|
Fast Distribution To Real Regression | Junier B. Oliva
; Willie Neiswanger
; Barnabas Poczos
; Jeff Schneider
; Eric Xing
; | Date: |
10 Nov 2013 | Abstract: | We study the problem of distribution to real-value regression, where one aims
to regress a mapping $f$ that takes in a distribution input covariate $Pin
mathcal{I}$ (for a non-parametric family of distributions $mathcal{I}$) and
outputs a real-valued response $Y=f(P) + epsilon$. This setting was recently
studied, and a "Kernel-Kernel" estimator was introduced and shown to have a
polynomial rate of convergence. However, evaluating a new prediction with the
Kernel-Kernel estimator scales as $O(N)$. This causes the difficult situation
where a large amount of data may be necessary for a low estimation risk, but
the computation cost of estimation becomes unfeasible when the data-set is too
large. To this end, we propose the Double-Basis estimator, which looks to
alleviate this big data problem in two ways: first, the Double-Basis estimator
is shown to have a computation complexity that is independent of the number of
of instances $N$ when evaluating new predictions after training; secondly, the
Double-Basis estimator is shown to have a fast rate of convergence for a
general class of mappings $finmathcal{F}$. | Source: | arXiv, 1311.2236 | Services: | Forum | Review | PDF | Favorites |
|
|
No review found.
Did you like this article?
Note: answers to reviews or questions about the article must be posted in the forum section.
Authors are not allowed to review their own article. They can use the forum section.
browser CCBot/2.0 (https://commoncrawl.org/faq/)
|
| |
|
|
|
| News, job offers and information for researchers and scientists:
| |