\dm_csml_event_details UCL ELLIS

Learning from Temporal Data Using Dynamical Feature Space


Speaker

Peter Tino

Affiliation

University of Birmingham

Date

Friday, 06 February 2015

Time

13:00-14:00

Location

Zoom

Link

Roberts G08 (Sir David Davies lecture theatre)

Event series

Jump Trading/ELLIS CSML Seminar Series

Abstract

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.

Biography