Computational Modelling Group

Machine learning

Even amongst practitioners, there is no truly well accepted definition for machine learning. So, I’ll to provide two:

  • Pioneer machine learning researcher Arthur Samuel defined machine learning as: “the field of study that gives computers the ability to learn without being explicitly programmed”. This definition is beautiful in its simplicity though lacks a little formality. So, with a little more structure,..
  • Tom Mitchell states that “a computer program is said to learn from experience E, with respect to some task T, and some performance measure P, if its performance on T as measured by P improves with experience E”.

A classic practical application is the email spam filter. The email program watches which emails the user does or does not mark as spam and, based on that, learns how to better filter future spam automatically. In the parlance of Tim Mitchell’s definition, classifying the emails as spam or not span is the task, T, watching the user label emails as spam or not spam is the experience, E, and the fraction emails correctly classified could be the perform measure, P.

There are a great number of machine learning algorithms and, as such, they are often divided into three main types: supervised, unsupervised and reinforcement learning algorithms.

For queries about this topic, contact Ash Booth.

View the calendar of events relating to this topic.

Projects

Automated Algorithmic Trading with Intelligent Execution

Frank McGroarty, Enrico Gerding (Investigators), Ash Booth

In this project, we introduce the first fully automated trading system for real-world stock trading that uses time-adaptive execution algorithm to minimise market impact while increasing profitability com- pared to benchmark strategies.

Automated Trading with Performance Weighted Random Forests and Seasonality

Frank McGroarty, Enrico Gerding (Investigators), Ash Booth

This project proposes an expert system that uses novel machine learning techniques to predict the price return over these seasonal events, and then uses these predictions to develop a profitable trading strategy.

Automatic Image Retrieval with Soft Biometrics for Surveillance

Mark Nixon, John Carter (Investigators), Daniel Martinho-Corbishley

We're investigating ways to automatically describe and identify pedestrians from surveillance footage using human understandable, soft biometric labels. Our goal is to enable surveillance operators to search for pedestrians in a video network using soft biometric descriptions, and to automatically retrieve these descriptions from CCTV images.

Centre for Doctoral Training in Next Generation Computational Modelling

Hans Fangohr, Ian Hawke, Peter Horak (Investigators), Susanne Ufermann Fangohr, Ryan Pepper, Hossam Ragheb, Emanuele Zappia, Ashley Setter, David Lusher, Alvaro Perez-Diaz, Kieran Selvon, Thorsten Wittemeier, Mihails Milehins, Stephen Gow, Ioannis Begleris, Jonathon Waters, James Harrison, Joshua Greenhalgh, Rory Brown, Robert Entwistle, Paul Chambers, Jan Kamenik, Craig Rafter

The £10million Centre for Doctoral Training was launched in November 2013 and is jointly funded by EPSRC, the University of Southampton, and its partners.

The NGCM brings together world-class simulation modelling research activities from across the University of Southampton and hosts a 4-year doctoral training programme that is the first of its kind in the UK.

Deep Optimisation

Jamie Caldwell

The project will develop the implementation and application of a new optimisation technique. 'Deep optimisation' combines deep learning techniques in neural networks with distributed optimisation methods to create a dynamically re-scalable optimisation process. This project will develop this technique to better-understand its capabilities and limitations and develop GPU implementations. The protein structure prediction problem will be used as the main test application.

Genetic studies to characterise the role of genetic factors in early-onset breast cancer

Andrew Collins (Investigator), Rosanna Upstill-Goddard

Breast cancer is a highly heterogeneous disease, with many distinct subtypes. In the majority of breast cancer cases the causative genetic component is poorly characterised. This study aims to explore both rare and common mutations in early-onset breast cancer patients and the contribution of such variants to disease using a variety of analytic approaches.

New Forest Cicada Project

Alexander Rogers, Geoff Merrett (Investigators), Davide Zilli, Oliver Parson

Rediscover the critically endangered New Forest cicada with crowdsourced smartphone biodiversity monitoring techniques.

On the applicability of nonlinear timeseries methods for partial discharge analysis

Paul Lewin (Investigator), Lyuboslav Petrov

The governing processes of Partial Discharge (PD)
phenomena trigger aperiodic chains of events resulting in ’ap-
parently’ stochastic data, for which the widely adopted analysis
methodology is of statistical nature. However, it can be shown,
that nonlinear analysis methods can prove more adequate in
detecting certain trends and patterns in complex PD timeseries.
In this work, the application of nonlinear invariants and phase
space methods for PD analysis are discussed and potential pitfalls
are identified. Unsupervised statistical inference techniques based
on the use of surrogate data sets are proposed and employed for
the purpose of testing the applicability of nonlinear algorithms
and methods. The Generalized Hurst Exponent and Lempel Ziv
Complexity are used for finding the location of the system under
test on the spectrum between determinism and stochasticity. The
algorithms are found to have strong classification abilities at
discerning between surrogates and original point series, giving
motivation for further investigations.

Optimisation of Acoustic Systems for Perceived Sound Quality

Jordan Cheer (Investigator), Daniel Wallace

Acoustic systems have traditionally been optimised on the basis of minimising an objective acoustic measure, such as sound pressure level. The project investigates the use of subjective measures of sound quality, such as "loudness", "harshness" etc. in optimisation algorithms.

Prediction of Psychopathology by MRT data

We aim to predict psychopathological outcomes in adults by functional brain data using multilevel regression and crossvaligdation strategies.

People

Andrew Collins
Professor, Medicine (FM)
Hans Fangohr
Professor, Engineering Sciences (FEE)
Paul Lewin
Professor, Electronics and Computer Science (FPAS)
Frank McGroarty
Professor, Management (FBL)
Mark Nixon
Professor, Electronics and Computer Science (FPAS)
Peter Horak
Reader, Optoelectronics Research Centre
John Carter
Senior Lecturer, Electronics and Computer Science (FPAS)
Nicholas Sheron
Senior Lecturer, Medicine (FM)
Jordan Cheer
Lecturer, Institute of Sound & Vibration Research (FEE)
Ian Hawke
Lecturer, Mathematics (FSHS)
Geoff Merrett
Lecturer, Electronics and Computer Science (FPAS)
Alexander Rogers
Lecturer, Electronics and Computer Science (FPAS)
Petros Bogiatzis
Research Fellow, Ocean & Earth Science (FNES)
Taihai Chen
Research Fellow, Electronics and Computer Science (FPAS)
Btissam Er-Rahmadi
Research Fellow, Management (FBL)
Ioannis Begleris
Postgraduate Research Student, Engineering Sciences (FEE)
Harry Beviss
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Ash Booth
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Rory Brown
Postgraduate Research Student, Civil Engineering & the Environment (FEE)
Jamie Caldwell
Postgraduate Research Student, Engineering Sciences (FEE)
Paul Chambers
Postgraduate Research Student, Engineering Sciences (FEE)
Evander DaCosta
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Samuel Diserens
Postgraduate Research Student, Engineering Sciences (FEE)
Robert Entwistle
Postgraduate Research Student, Engineering Sciences (FEE)
Stephen Gow
Postgraduate Research Student, Engineering Sciences (FEE)
Joshua Greenhalgh
Postgraduate Research Student, Engineering Sciences (FEE)
James Harrison
Postgraduate Research Student, Engineering Sciences (FEE)
Reza J. Forooshani
Postgraduate Research Student, Medicine (FM)
Jan Kamenik
Postgraduate Research Student, Engineering Sciences (FEE)
Konstantinos Kouvaris
Postgraduate Research Student, Electronics and Computer Science (FPAS)
David Lusher
Postgraduate Research Student, Engineering Sciences (FEE)
Alvaro Perez-Diaz
Postgraduate Research Student, Engineering Sciences (FEE)
Lyuboslav Petrov
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Craig Rafter
Postgraduate Research Student, Engineering Sciences (FEE)
Hossam Ragheb
Postgraduate Research Student, Engineering Sciences (FEE)
Sabin Roman
Postgraduate Research Student, University of Southampton
Kieran Selvon
Postgraduate Research Student, Engineering Sciences (FEE)
Ashley Setter
Postgraduate Research Student, Engineering Sciences (FEE)
Nathan Smith
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Daniel Wallace
Postgraduate Research Student, Engineering Sciences (FEE)
Jonathon Waters
Postgraduate Research Student, Engineering Sciences (FEE)
Thorsten Wittemeier
Postgraduate Research Student, Engineering Sciences (FEE)
Emanuele Zappia
Postgraduate Research Student, Engineering Sciences (FEE)
Davide Zilli
Postgraduate Research Student, Electronics and Computer Science (FPAS)
Izidor Flajsman
Undergraduate Research Student, Electronics and Computer Science (FPAS)
Jess Jones
Technical Staff, iSolutions
Susanne Ufermann Fangohr
Administrative Staff, Civil Engineering & the Environment (FEE)
Arthur Lugtigheid
Alumnus, Psychology (FSHS)
Mihails Milehins
Alumnus, University of Southampton
Oliver Parson
Alumnus, Electronics and Computer Science (FPAS)
Mohamed Bakoush
None, None
Enrico Gerding
None, None
Iris Kramer
None, None