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- Ming-Jie Zhao, Narayanan Unny Edakunni, Adam Craig Pocock, Gavin Brown
- Journal of Machine Learning Research
- 2013

Fano’s inequality lower bounds the probability of transmission error through a communication channel. Applied to classification problems, it provides a lower bound on the Bayes error rate and motivates the widely used Infomax principle. In modern machine learning, we are often interested in more than just the error rate. In medical diagnosis, different… (More)

- Nikolaos Nikolaou, Narayanan Unny Edakunni, Meelis Kull, Peter A. Flach, Gavin Brown
- Machine Learning
- 2016

We provide a unifying perspective for two decades of work on cost-sensitive Boosting algorithms. When analyzing the literature 1997–2016, we find 15 distinct cost-sensitive variants of the original algorithm; each of these has its own motivation and claims to superiority—so who should we believe? In this work we critique the Boosting literature using four… (More)

We present a Bayesian formulation of locally weighted learning (LWL) using the novel concept of a randomly varying coefficient model. Based on this, we propose a mechanism for multivariate non-linear regression using spatially localised linear models that learns completely independent of each other, uses only local information and adapts the local model… (More)

We present a probabilistic formulation of UCS (a sUpervised Classifier System). UCS is shown to be a special case of mixture of experts where the experts are learned independently and later combined during prediction. In this work, we develop the links between the constituent components of UCS and a mixture of experts, thus lending UCS a strong analytical… (More)

- Narayanan Unny Edakunni, Gary Brown, Tim Kovacs
- UAI
- 2011

In this paper, we derive a novel probabilistic model of boosting as a Product of Experts. We re-derive the boosting algorithm as a greedy incremental model selection procedure which ensures that addition of new experts to the ensemble does not decrease the likelihood of the data. These learning rules lead to a generic boosting algorithm POEBoost which turns… (More)

We present a novel ensemble of logistic linear regressors that combines the robustness of online Bayesian learning with the flexibility of ensembles. The ensemble of classifiers are built on top of a Randomly Varying Coefficient model designed for online regression with the fusion of classifiers done at the level of regression before converting it into a… (More)

- Narayanan Unny Edakunni, Gavin Brown, Tim Kovacs
- GECCO
- 2011

In recent years there have been efforts to develop a probabilistic framework to explain the workings of a Learning Classifier System. This direction of research has met with limited success due to the intractability of complicated heuristic training rules used by the learning classifier systems. In this paper, we derive a learning classifier system from a… (More)

Research in transportation frequently involve modelling and predicting attributes of events that occur at regular intervals. The event could be arrival of a bus at a bus stop, the volume of a traffic at a particular point, the demand at a particular bus stop etc. In this work, we propose a specific implementation of probabilistic graphical models to learn… (More)

- Narayanan Unny Edakunni, Graham McNeill, Giorgos Petkos, Timothy M. Hospedales, Adrian Haith, Matthew Howard
- 2009

Locally weighted regression is a non-parametric technique of regression that is capable of coping with non-stationarity of the input distribution. Online algorithms like Receptive Field Weighted Regression and Locally Weighted Projection Regression use a sparse representation of the locally weighted model to approximate a target function, resulting in an… (More)

- Tim Kovacs, Narayanan Unny Edakunni, Gavin Brown
- GECCO
- 2011

UCS is a Learning Classifier System (LCS) which evolves condition-action rules for supervised classification tasks. In UCS the fitness of a rule is based on its accuracy raised to a power ν, and this fitness is used in both the search for good rules (via a genetic algorithm) and in a classification vote. We trace the origin of the UCS fitness function… (More)

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