The aim of this workshop is to consolidate research efforts in this area, and to provide an informal discussion forum for researchers and practitioners interested in this important yet diverse subject. The discussion will revolve around two main themes, which basically correspond to the two fundamental questions that arise when abandoning the realm of vectorial, feature-based representations, namely:

• How can one obtain suitable similarity information from data representations that are more powerful than, or simply different from, the vectorial?
• How can one use similarity information in order to perform learning and classification tasks?

We aim at covering a wide range of problems and perspectives, from supervised to unsupervised learning, from generative to discriminative models, and from theoretical issues to real-world practical applications.

Accordingly, topics of interest include (but are not limited to):

• Embedding and embeddability
• Graph spectra and spectral geometry
• Indefinite and structural kernels
• Game-theoretic models of pattern recognition
• Characterization of non-(geo)metric behaviour
• Foundational issues
• Measures of (geo)metric violations
• Learning and combining similarities
• Multiple-instance learning
• Applications

Detailed information can be found at SIMBAD 2011.