Seminar: Learning from Temporal Data Using Dynamical Feature Space

SpeakerPeter Tino
AffiliationUniversity of Birmingham
DateFriday, 06 Feb 2015
Time13:00 - 14:00
LocationRoberts G08 (Sir David Davies lecture theatre)
Event seriesMicrosoft Research CSML Seminar Series

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 filter 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 temporal filters. The methodology will be demonstrated in a series of sequence classification tasks and in an incremental temporal "regime" detection task.

iCalendar csml_id_210.ics