Day 1 - September 17, 2012

Invited Talk #1

NeuCube EvoSpike Architecture for Spatio-Temporal Modelling and Pattern Recognition of Brain Signals
Nik Kasabov

S1 - Data description

    1. How to Quantitatively Compare Data Dissimilarities for Unsupervised Machine Learning?
      Bassam Mokbel, Sebastian Gross, Markus Lux, Niels Pinkwart and Barbara Hammer
    2. Representative Prototype Sets for Data Characterization and Classification
      Ludwig Lausser, Christoph Müssel and Hans Kestler
    3. On Graph-Associated Matrices and Their Eigenvalues for Optical Character Recognition
      Miriam Schmidt, Günther Palm and Friedhelm Schwenker

S2 - Learning Paradigms/algorithms (#1 and #2)
14.30-15.45 - S2a

      1. Kernel Robust Soft Learning Vector Quantization
        Daniela Hofmann and Barbara Hammer
      2. Gradient Algorithms for Exploration/Exploitation Trade-Offs: Global and Local Variants
        Michel Tokic and Günther Palm
      3. Towards a Novel Probabilistic Graphical Model of Sequential Data: Fundamental Notions and a Solution to the Problem of Parameter Learning
        Edmondo Trentin and Marco Bongini

16.10-17.00 - S2b

      1. Towards a Novel Probabilistic Graphical Model of Sequential Data: a Solution to the Problem of Structure Learning and an Empirical Evaluation
        Marco Bongini and Edmondo Trentin
      2. Statistical Recognition Of a Set Of Patterns Using Novel Probability Neural Network
        Andrey V. Savchenko