Discriminative State-Space Models

被引：0

作者：

Kuznetsov, Vitaly ^{[1
]}

Mohri, Mehryar ^{[1
,2
]}

机构：

[1] Google Res, New York, NY 10011 USA

[2] Courant Inst, New York, NY 10011 USA

来源：

ADVANCES IN NEURAL INFORMATION PROCESSING SYSTEMS 30 (NIPS 2017) | 2017年 / 30卷

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We introduce and analyze Discriminative State-Space Models for forecasting non-stationary time series. We provide data-dependent generalization guarantees for learning these models based on the recently introduced notion of discrepancy. We provide an in-depth analysis of the complexity of such models. We also study the generalization guarantees for several structural risk minimization approaches to this problem and provide an efficient implementation for one of them which is based on a convex objective.

引用

页数：9

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