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 |
DeepMind/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 |