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- Mark A. Girolami, Ata Kabán
- SIGIR
- 2003

Latent Dirichlet Allocation (LDA) is a fully generative approach to language modelling which overcomes the inconsistent generative semantics of Probabilistic Latent Semantic Indexing (PLSI). This… (More)

- Ata Kabán
- Pattern Recognition Letters
- 2007

We present a new classification approach, using a variational Bayesian estimation of probit regression with Laplace priors. Laplace priors have been previously used extensively as a sparsity-inducing… (More)

- Ata Kabán, Mark A. Girolami
- IEEE Trans. Pattern Anal. Mach. Intell.
- 2001

We present a general framework for data analysis and visualisation by means of topographic organization and clustering Imposing distributional assumptions on the assumed underlying latent factors… (More)

- Jakramate Bootkrajang, Ata Kabán
- ECML/PKDD
- 2012

The classical problem of learning a classifier relies on a set of labelled examples, without ever questioning the correctness of the provided label assignments. However, there is an increasing… (More)

- Jakramate Bootkrajang, Ata Kabán
- Bioinformatics
- 2013

MOTIVATION
Previous studies reported that labelling errors are not uncommon in microarray datasets. In such cases, the training set may become misleading, and the ability of classifiers to make… (More)

- Robert J. Durrant, Ata Kabán
- J. Complexity
- 2009

Beyer et al. gave a sufficient condition for the high dimensional phenomenon known as the concentration of distances. Their work has pinpointed serious problems due to nearest neighbours not being… (More)

- Ata Kabán
- AISTATS
- 2014

In this paper we provide a new analysis of compressive least squares regression that removes a spurious log N factor from previous bounds, where N is the number of training points. Our new bound has… (More)

- Jakramate Bootkrajang, Ata Kabán
- UAI
- 2013

Boosting is known to be sensitive to label noise. We studied two approaches to improve AdaBoost’s robustness against labelling errors. One is to employ a label-noise robust classifier as a base… (More)

- Robert J. Durrant, Ata Kabán
- Machine Learning
- 2014

We prove theoretical guarantees for an averaging-ensemble of randomly projected Fisher linear discriminant classifiers, focusing on the case when there are fewer training observations than data… (More)

- Ella Bingham, Ata Kabán, Mark A. Girolami
- Neural Processing Letters
- 2003

The problem of analysing dynamically evolving textual data has arisen within the last few years. An example of such data is the discussion appearing in Internet chat lines. In this Letter a recently… (More)