Accepted paper at ESANN'2015

The paper about Probabilistic Classification Vector Machine at large scale was accepted as
oral presentation at the European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. In the paper we present an alternative formulation of the Probabilistic Classification Vector Machine (PCVM) to achieve linear runtime complexity.

Journal publication about a generic probabilistic classifier (also applicable for non-metric proximity data)

The paper Generic probabilistic prototype based classification of vectorial and proximity data
published at Neurocomputing is now online. In supervised learning probabilistic models are attractive to define discriminative models in a rigid mathematical framework. More recently, prototype approaches, known for compact and efficient models, were defined in a probabilistic setting, but are limited to metric vectorial spaces. Here we propose a generalization of the discriminative probabilistic prototype learning algorithm for arbitrary proximity data, widely applicable to a multitude of data analysis tasks. We extend the algorithm to incorporate adaptive distance measures, kernels and non-metric proximities in a full probabilistic framework.

Benchmark sets for indefinite proximity learning

At the page Benchmark I have provided a matlab file with 13 proximity benchmarks
at various complexity, scale and from different application domains. The page contains
also a description and references to the original work behind the datasets.

journal publication on conformal prediction for proximity data

The paper Adaptive Conformal Semi-Supervised Vector Quantization for Dissimilarity Data
published at Pattern Recognition is now online. We propose a conformal classifier for
multi-class semi-supervised learning of proximity data.
Update: The article won the College best publication award of the University of Birmingham College of Engineering and Physical Sciences

Publication at ICANN 2014

Accepted paper at ICANN 2014 about Discriminative Fast Soft Competitive Learning
The approach extends relational Soft Competitive Learning (aka Relational Neural Gas) by a supervised learning scheme which is more reliable than former approaches. The method can
be used to get a (semi-) supervised clustering of (large) proximity matrices.

Embedding of potentially asymmetric proximity data (visualization)

Published a paper with Marc Strickert and Eyke Hüllermeier about

Correlation-based embedding of pairwise score data

The approach can be applied to embed potentially asymmetric proximity (score) data preserving

correlation between the data. It also shows multiple theoretical insights into the conceptual behavior of alternative approaches. 

http://www.sciencedirect.com/science/article/pii/S0925231214003956

Published paper at Neurocomputing

Published a paper "Learning vector quantization for (dis-)similarities"

focusing on a framework for (non-metric) proximity based learning using prototypes.
http://www.sciencedirect.com/science/article/pii/S0925231213010941