Live Projects 






SMADA  Smart Data Analytics 
Machine Learning 'in the wild' 
Machine Reasoning 
Generalised Maxentropy Classifiers 
The goal of SMADA is to develop a nextgeneration, platformindependent collaborative big data environment designed to act as a space in which both existing and newly developed analytical tools are considered as modules that can be interconnected to generate new tools, thereby allowing small and large companies to safely share their knowledge pool.


Our goal is a blue sky rethinking of machine learning, laying the foundations for an entirely new, inherently robust
theory of learning. Statistical learning theory is generalised to allow for test and training data to come from distinct
probability distributions. We move away from the selection of single models to that of convex sets of models, and
employ the resulting theory to lay solid theoretical foundations for deep learning. 


We are working on a generalised maximumentropy classification framework,
in which the empirical expectation of the feature functions is bounded by the lower and
upper expectations associated with the lower and upper probabilities associated with a belief
measure. This generalised setting permits a more cautious appreciation of the information
content of a training set.


Past Projects 





Metric learning 
Tensor classification 
Vehicle classification from inductive loop signature 
We devised a general framework for learning
distance functions for generative dynamical models, given a training set of labelled
videos. The optimal distance function is selected among a family of pullback ones,
induced by a parameterised automorphism of the space of models. We focus here on
hidden Markov models and their manifold, and design an appropriate automorphism
there. Experimental results are presented which show how pullback learning greatly
improves action recognition performances with respect to base distances.


In most realworld problem however, observations are influenced by a number of nuisance factors. To tackle their influence,
it is natural to resort to multilinear or "tensorial" decompositions.
We show how HOSVD can be exploited to formulate a natural generalization of Tenenbaum's bilinear classifiers, which we
call 'multilinear classifiers', able to classify observations depending on one content label and several style labels.


Inductive loops are sensors that are widely deployed on road networks for the purpose
of traffic data collection. Our aim is to classify vehicles in a 10 category scheme such as the SWISS10 from
inductive loop signals.
We looked at two machinelearning algorithms: Support Vector Machines and Adaptive
Boosting with decision stumps. We used the two most common algorithms for multiclass
classification, OneversusOne and OneversusRest, and we looked at addressing
classimbalance with Undersampling, Oversampling and SMOTE.

