Bounds for Vector-Valued Function Estimation
Machine Learning
2016-06-07 v1 Machine Learning
Abstract
We present a framework to derive risk bounds for vector-valued learning with a broad class of feature maps and loss functions. Multi-task learning and one-vs-all multi-category learning are treated as examples. We discuss in detail vector-valued functions with one hidden layer, and demonstrate that the conditions under which shared representations are beneficial for multi- task learning are equally applicable to multi-category learning.
Cite
@article{arxiv.1606.01487,
title = {Bounds for Vector-Valued Function Estimation},
author = {Andreas Maurer and Massimiliano Pontil},
journal= {arXiv preprint arXiv:1606.01487},
year = {2016}
}