Thursday 08 Nov 2018: Learning from Temporal Data Through Learning in the Space of Dynamical Systems
Prof. Peter Tino - University of Birmingham
Harrison 170 14:30-15:30
In learning from "static" data (order of data presentation does not carry any useful information), one framework for dealing with such data is to transform the input items non-linearly into a feature space (usually high-dimensional), that is "rich" enough, so that linear techniques are sufficient. However, data such as EEG signals, or biological sequences naturally comes with a sequential structure. I will present a general dynamical state space model that effectively acts as a dynamical feature space for representing temporally ordered samples. I will then outline a framework for learning on sets of sequential data by building kernels based such dynamical filters. The methodology will be demonstrated in a series of sequence classification tasks and in an incremental temporal "regime" detection task.