PCVM C++/armadillo library 0.2

New version of the PCVM C++ code is now available including
the usage of Nystroem approximated kernel (see Readme).
The code now also contains concepts published at ESANN 2015 see
http://www.promos-science.blogspot.de/2015/01/accepted-paper-at-esann2015.html

It may also be interesting if you would like to have a C++/Armadillo implementation
of a Nystroem approximated singular value decomposition (SVD), eigenvalue decomposition (EVD)
or pseudo inverse calculation (PINV). Further it contains code for the normCDF and normPDF.

The code is available at PCVM code

C++ / Armadillo  multicore code for Probabilistic Classification Machine (PCVM) published
see PCVM code

The paper about Incremental Probabilistic Classification Vector
Machine with linear costs was accepted at the International Joint Conference on Neural Networks. In the paper we improve the runtime and memory complexity of the Incremental Probabilistic Classification Vector Machine (EPCVM) to linear costs and derive a formulation for a Nystroem approximation on asymmetric matrices.

Tutorial at IJCNN 2015: Learning in indefinite proximity spaces: Mathematical foundations, representations, and models

I will present a tutorial about Learning in indefinite proximity spaces:
Mathematical foundations, representations, and models. The announcement
and content description of the tutorial is available here
Full material will follow in the next weeks.

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.