Abstract
This introductory chapter describes the SIMBAD project, which represents the first systematic attempt at bringing to full maturation a paradigm shift that is just emerging within the pattern recognition and machine learning domains, where researchers are becoming increasingly aware of the importance of similarity information per se, as opposed to the classical (feature-based) approach.
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Notes
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From: Artificial Cognitive Systems in FP7: A Report on Expert Consultations for the EU Seventh Framework Programme 2007–2013 for Research and Technology Development.
- 2.
A set of distances D is said to be Euclidean (or geometric) if there exists a configuration of points in some Euclidean space whose interpoint distances are given by D. In the sequel, the terms geometric and Euclidean will be used interchangeably. The term (geo)metric is an abbreviation to indicate the case of a distance that satisfies either the Euclidean or the metric properties.
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Pelillo, M. (2013). Introduction: The SIMBAD Project. In: Pelillo, M. (eds) Similarity-Based Pattern Analysis and Recognition. Advances in Computer Vision and Pattern Recognition. Springer, London. https://doi.org/10.1007/978-1-4471-5628-4_1
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