SIMBAD 2015 – University of Copenhagen


3rd International Workshop on Similarity-Based Pattern Analysis and Recognition will be held in Copenhagen.

Traditional pattern recognition and machine learning techniques are intimately linked to the notion of "feature space." Adopting this view, each object is described in terms of a vector of numerical attributes and is therefore mapped to a point in a Euclidean vector space so that the distances between the points reflect the observed (dis)similarities between the respective objects. This kind of representation is attractive because such spaces offer powerful analytical as well as computational tools that are simply not available in other representations. This approach, however, suffers from a major intrinsic limitation, which concerns the representational power of vectorial, feature-based descriptions. In fact, there are numerous application domains where either it is not possible to find satisfactory features or they are inefficient for learning purposes.

Invited speakers:

More info on the official SIMBAD 2015 conference homepage

Targeted to researchers within machine learning og pattern recognition.