1) Are Extreme Learning Machines as fast to train as claimed?
The Extreme Learning Machine (ELM) is a single hidden layer artificial neural network (ANN)
that is very useful for regression and classification problems. A characteristic of the ELM is
that only the output layer is optimised using training data which can result in fast training
times. A drawback of the ELM is that the size of network required for a given task tends to
be much larger than an ANN with all layers optimised. This slows down the training time of
the ELM. In this project the researcher will first implement algorithms for the ELM and the
fully optimised ANN and then perform a side-by-side comparison of the performance and
training times of the networks. The researcher will evaluate the networks on standard
publically available databases such as the MNIST handwriting recognition and other
databases available from the University of California machine learning repository.
Background knowledge required: Signal processing and pattern recognition. An interest in
machine learning methods. Matlab.
Number of students: 1
2) What is the best error function for Extreme Learning Machines performing classification
tasks?
The Extreme Learning Machine (ELM) is a single hidden layer artificial neural network (ANN)
that is very useful for practical regression and classification problems. It has proven a
popular method for training ANNs due to its speed of training. The error (or cost) function
used by ELMs is the sum of squares (SSE) function. While this is optimal for regression
problems it is not optimal for classification problems. In this project the researcher will
investigate using alternative and more appropriate cost functions for the ELM for
classification problems. Examples of such cost functions appropriate for classification
problems include the multinomial logistic, linear, quadratic discriminant analysis and
correntropy cost functions. The researcher will implement the alternative cost functions in
the ELM training algorithm and then compare the classification performance against the SSE
cost function on a number of databases from the University of California machine learning
repository.
Background knowledge required: Signal processing and pattern recognition. A good
knowledge of partial differentiation. An interest in machine learning methods. Matlab.
Number of students: 1
3) Boosting the performance of an ECG classification system by learning from labelled and
unlabelled data
Semi-supervised learning is an area of machine learning that seeks to learn with labelled and
unlabelled data and train systems that provide higher overall performance than systems
using labelled data only. This project will apply semi-supervised learning to the problem of
electrocardiogram (ECG) heart beat classification. The motivation for this project is that
while labelled ECG data is expensive to obtain, unlabelled ECG data is easy and cheap to
acquire so semi-supervised learning could offer a cheap way of boosting performance of ECG
classifiers.
Background knowledge required: Biomedical signal processing and pattern recognition. An
interest in developing software for biomedical instrumentation. Matlab.
Number of students: 1
4) Measuring respiration from the photoplethysmogram to aid the diagnosis of sleep apnoea
The photoplethysmogram (PPG) signal is widely used by the medical community to measure
oxygen saturation and heart rate as it is a cheap and highly convenient sensor. In this project
we will look at extending the capability of the sensor to measure respiration rate and
amplitude. It is anticipated that this will enable the sensor to accurately identify sleep
disordered breathing and thus provide a highly convenient diagnostic tool for the detection
of sleep apnoea.
Background knowledge required: Biomedical signal processing including filter design. An
interest in biomedical instrumentation. Matlab.
Number of students: 1
5) Screening patients for sleep apnoea by processing photographic images of the face
Sleep apnoea is a disease where a patient repetitively stops breathing during sleep. It affects
approximately 5-10% of the general population. Recent research has highlighted the
possibility of detecting people at risk for sleep apnoea by identifying craniofacial
abnormalities that may resulting in a compromised airway space and an increase in upper
airway collapsibility. This project will use a database of facial images from patients with
known sleep apnoea classification and develop image processing algorithms to estimate the
likelihood the patient has sleep apnoea.
Background knowledge required: Image processing. An interest in biomedical image
processing. Matlab.
Number of students: 1