arXiv Open Access 2016

Bounds for Vector-Valued Function Estimation

Andreas Maurer Massimiliano Pontil

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Abstrak

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.

Topik & Kata Kunci

stat.ML cs.LG

Penulis (2)

Andreas Maurer

Massimiliano Pontil

Format Sitasi

APA MLA BibTeX

Maurer, A., Pontil, M. (2016). Bounds for Vector-Valued Function Estimation. https://arxiv.org/abs/1606.01487

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Informasi Jurnal

Tahun Terbit: 2016
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓