Applying a kernel function on time-dependent data to provide supervised-learning guarantees

作者：

Highlights：

• We employ a Monte-Carlo approach to find the best phase space for a given data stream.

• We propose kFTCV, a novel approach to validate data stream classification.

• Results show Taken’s theorem can transform data streams into independent states.

• Therefore, we can rely on SLT framework to ensure learning when dealing with data streams.

摘要

We employ a Monte-Carlo approach to find the best phase space for a given data stream.•We propose kFTCV, a novel approach to validate data stream classification.•Results show Taken’s theorem can transform data streams into independent states.•Therefore, we can rely on SLT framework to ensure learning when dealing with data streams.

论文关键词：Statistical Learning Theory,Time dependency,Kernel function,Takens’ immersion theorem,Supervised-learning algorithms

论文评审过程：Received 28 July 2016, Revised 28 October 2016, Accepted 19 November 2016, Available online 25 November 2016, Version of Record 1 December 2016.

论文官网地址：https://doi.org/10.1016/j.eswa.2016.11.028