Image Ranking with Eye Movements (2009)
Pasupa, Kitsuchart, Szedmak, Sandor, Hardoon, David
In order to help users navigate an image search system, one could provide explicit rank information on a set of images. These rankings are learnt so to present a new set of relevant images. Although,...
A Nonconformity Approach to Model Selection for SVMs (2009)
Hardoon, David, Hussain, Zakria, Shawe-Taylor, John
We investigate the issue of model selection and the use of the nonconformity (strangeness) measure in batch learning. Using the nonconformity measure we propose a new training algorithm that helps...
Matching Pursuit Kernel Fisher Discriminant Analysis (2009)
Diethe, Tom, Hussain, Zakria, Hardoon, David, Shawe-Taylor, John
We derive a novel sparse version of Kernel Fisher Discriminant Analysis (KFDA) using an approach based on Matching Pursuit (MP). We call this algorithm Matching Pursuit Kernel Fisher Discriminant...
Correlation Based Multivariate Analysis of Genetic Influence on Brain Volume (2009)
Hardoon, David, Ettinger, Ulrich, Mourao-Miranda, Janaina, Antonova, Elena, Collier, David, Kumari, Veena, ...
Considerable research effort has focused on achieving a better understanding of the genetic individual differences in volumetric and morphological brain measures. The importance of these is...
Chen, Chih-Chun, Hardoon, David
This paper presents a novel approach towards showing how specific emergent multi-level behaviours in agent-based simulations can be quantified and used as the basis for inferring predictive models....
Stability Analysis of Kernel Canonical Correlation Analysis: Theory and Practice (2008)
Hardoon, David, Shawe-Taylor, John
Canonical Correlation Analysis is a technique for finding pairs of basis vectors that maximise the correlation of a set of paired variables, these pairs can be considered as two views of the same...
Correlation Based Multivariate Analysis of the Genetic Influence on Brain Volume (2008)
Hardoon, David, Ettinger, Ulrich, Mourao-Miranda, Janaina, Antonova, Elena, Collier, David, Kumari, Veena, ...
Considerable research effort has focused on achieving a better un- derstanding of the genetic correlates of individual differences in vol- umetric and morphological brain measures. The importance...
Text classification with a Primal SVM endowed with domain knowledge (2008)
Parrado-Hernandez, Emilio, Hardoon, David
In this paper we solve a document classification task by incorporating prior/domain knowledge onto the SVM. The algorithm consists in to learn a prior classifier in the primal space (words) from an...
Using String Kernels to Identify Famous Performers from their Playing Style (2008)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Using String Kernels to Identify Famous Performers from their Playing Style (2008)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Using String Kernels to Identify Famous Performers from their Playing Style (2008)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Using String Kernels to Identify Famous Performers from their Playing Style (2008)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Durrant, Simon, Hardoon, David, Brechmann, Andre, Shawe-Taylor, John, Miranda, Edurado, Scheich, Henning
This paper gives both general linear model (GLM) and support vector machine (SVM) analyses of an experiment concerned with tonality in music. The two forms of analysis are both contrasted and used to...
PAC-Bayes Analysis of Maximum Entropy Learning (2008)
Shawe-Taylor, John, Hardoon, David
We extend and apply the PAC-Bayes theorem to the analysis of maximum entropy learning by considering maximum entropy classification. The theory introduces a multiple sampling technique that controls...
Accounting for Voxel Neighbourhood Relationship in the SVM (2008)
Hardoon, David, Mourão-Miranda, Janaina, Rocha Rego, Vanessa, Shawe-Taylor, John
In Neuroimage data analysis there are several preprocessing stages that must be applied before any statistical analysis can be done. The sMRI scan preprocessing procedures usually include...
One Class SVM for Predicting Brain State (2008)
Mourão-Miranda, Janaina, Hardoon, David, Sato, Joao, Brammer, Michael
One aim of brain imaging studies of visual perception is to characterize neural codes for abstract percepts such as faces, non-face body parts, places, and objects. A common approach is to apply the...
Johnston, P, Hardoon, David, Ecker, C, Clarke, T, Powell, J, Murphy, D
Autistic spectrum disorders (ASDs) are moderately common, highly heritable neurodevelopmental conditions with a strong genetic basis. Several lines of evidence support genetic factors as a...
The Double-Barrelled LASSO (2008)
Hardoon, David, Shawe-Taylor, John
We present a new method which solves a double-barelled LASSO in a convex least squares approach. In the presented method we focus on the scenario where one is interested in (or limited to) a primal...
Multiview Learning with Labels (2008)
Diethe, Tom, Hardoon, David, Shawe-Taylor, John
CCA can be seen as a multiview extension of PCA, in which information from two sources is used for learning by finding a subspace in which the two views are most correlated. However PCA, and by...
Sparse Canonical Correlation Analysis (2008)
Hardoon, David, Shawe-Taylor, John
In this paper we present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when...
Can style be learned? A machine learning approach towards ‘performing’ as famous pianists (2007)
Dorard, Louis, Hardoon, David, Shawe-Taylor, John
In this paper a novel method for performing music in the style of famous pianists is presented. We use Kernel Canonical Correlation Analysis (KCCA), a method which looks for a common semantic...
Sparse Canonical Correlation Analysis (2007)
Hardoon, David, Shawe-Taylor, John
In this paper we present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when...
Sparse CCA for Bilingual Word Generation (2007)
Hardoon, David, Shawe-Taylor, John
Proposed by Hotelling 1936, Canonical Correlation Analysis (CCA) is a technique for finding pairs of basis vectors that maximises the correlation between a set of paired variables. The set of paired...
Using String Kernels to Identify Famous Performers from their Playing Style (2007)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Using Image Stimuli to Drive fMRI Analysis (2007)
Hardoon, David, Mourao-Miranda, Janaina, Brammer, Michael, Shawe-Taylor, John
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods that are increasingly being employed in...
Stability Analysis of Kernel Canonical Correlation Analysis: Theory and Practice (2007)
Hardoon, David, Shawe-Taylor, John
Canonical Correlation Analysis is a technique for finding pairs of basis vectors that maximise the correlation of a set of paired variables, these pairs can be considered as two views of the same...
A metamorphosis of Canonical Correlation Analysis into Multivariate Maximum Margin Learning (2007)
Szedmak, Sandor, De Bie, Tijl, Hardoon, David
Canonical Correlation Analysis(CCA) is a useful tool to discover relationship between different sources of information represented by vectors. The solution of the underlying optimisation problem...
Unsupervised analysis of fMRI data using Kernel Canonical Correlation (2007)
Hardoon, David, Mourao-Miranda, Janaina, Brammer, Michael, Shawe-Taylor, John
We introduce a new unsupervised fMRI analysis method based on Kernel Canonical Correlation Analysis which differs from the class of supervised learning methods ( e,g the Support Vector Machine) that...
Sparse Canonical Correlation Analysis (2007)
Hardoon, David, Shawe-Taylor, John
In this paper we present a novel method for solving Canonical Correlation Analysis (CCA) in a sparse convex framework using a least squares approach. The presented method focuses on the scenario when...
Information Retrieval by Inferring Implicit Queries from Eye Movements (2006)
Hardoon, David, Shawe-Taylor, John, Ajanki, Antti, Puolamäki, Kai, Kaski, Samuel
We introduce a new search strategy, in which the information retrieval (IR) query is inferred from eye movements measured when the user is reading text during an IR task. In training phase, we know...
Unsupervised fMRI Analysis (2006)
Hardoon, David, Mourao-Miranda, Janaina, Brammer, Michael, Shawe-Taylor, John
Recently machine learning methodology has been used increasing to analyze the relationship between stimulus categories and fMRI responses. Here, we introduce a new unsupervised machine learning...
Using String Kernels to Identify Famous Performers from their Playing Style (2006)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this chapter we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Semantic Models for Machine Learning (2006)
In this thesis we present approaches to the creation and usage of semantic models by the analysis of the data spread in the feature space. We aim to introduce the general notion of using feature...
Two view learning: SVM-2K, Theory and Practice (2005)
Farquhar, Jason, Hardoon, David, Meng, Hongying, Shawe-Taylor, John, Szedmak, Sandor
Kernel methods make it relatively easy to define complex high-dimensional feature spaces. This raises the question of how we can identify the relevant subspaces for a particular learning task. When...
Using String Kernels to Identify Famous Performers from their Playing Style (2005)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
fMRI Analysis via One-class Machine Learning Techniques (2005)
Hardoon, David, Manevitz, Larry M.
We show how one-class compression Neural Networks and one-class SVM can be applied to fMRI data to learn the classification of brain activity associated with a specific motor activity. For comparison...
Generic object recognition by combining distinct features in machine learning (2005)
Meng, Hongying, Hardoon, David, Shawe-Taylor, John, Szedmak, Sandor
In a generic image object recognition or categorization system, the relevant features or descriptors from a characteristic point, patch or region of an image are often obtained by different...
Hardoon, David, Saunders, Craig, Shawe-Taylor, John
We present a novel kernel which operates directly on the structural data of music notation. The characteristics of the composers writing style are obtained from note changes on a basic beat level,...
Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004)
Hardoon, David, Szedmak, Sandor, Shawe-Taylor, John
We present a general method using kernel canonical correlation analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation...
KCCA Feature Selection for fMRI Analysis (2004)
Hardoon, David, Shawe-Taylor, John, Friman, Ola
We use Kernel Canonical Correlation Analysis (KCCA) to infer brain activity in functional MRI by learning a semantic representation of fMRI brain scans and their associated activity signal. The...
Hardoon, David, Shawe-Taylor, John, Friman, Ola
Understanding the functional processes of the brain is still a new and difficult task. Functional Magnetic Resonance Imaging (fMRI) is a relatively new tool with the purpose of mapping the sensor,...
Multiclass classification by L1 norm Support Vector Machine (2004)
Szedmak, Sandor, Shawe-Taylor, John, Saunders, Craig, Hardoon, David
Multiclass classification by L1 norm Support Vector Machine (2004)
Szedmak, Sandor, Shawe-Taylor, John, Saunders, Craig, Hardoon, David
Using String Kernels to Identify Famous Performers from their Playing Style (2004)
Saunders, Craig, Hardoon, David, Shawe-Taylor, John, Widmer, Gerhard
In this paper we show a novel application of string kernels: that is to the problem of recognising famous pianists from their style of playing. The characteristics of performers playing the same...
Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004)
Hardoon, David, Szedmak, Sandor, Shawe-Taylor, John
We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation...
Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004)
Hardoon, David, Szedmak, Sandor, Shawe-Taylor, John
We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation...
Canonical Correlation Analysis: An Overview with Application to Learning Methods (2004)
Hardoon, David, Szedmak, Sandor, Shawe-Taylor, John
We present a general method using kernel Canonical Correlation Analysis to learn a semantic representation to web images and their associated text. The semantic space provides a common representation...
Signal Extraction for Brain-Computer Interface (2003)
Hardoon, David, Shawe-Taylor, John
We use Kernel Canonical Correlation Analysis (KCCA) for detecting brain activity in function MRI by learning a semantic representation of fMRI brain scans and their associated time frequency. The...
Signal Extraction for Brain-Computer Interface (2003)
Hardoon, David, Shawe-Taylor, John
Understanding the functional processes of the brain is still a new and difficult task. Functional Magnetic Resonance Imaging (fMRI) is a relatively new tool with the purpose of mapping the sensor,...
A Correlation Approach for Automatic Image Annotation (0006)
Hardoon, David, Saunders, Craig, Szedmak, Sandor, Shawe-Taylor, John
Abstract. The automatic annotation of images presents a particularly complex problem for machine learning researchers. In this work we experiment with semantic models and multi-class learning for the...