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Lecture Notes in Computer Science
We review the information-geometric framework for statistical pattern recognition: First, we explain the role of statistical similarity measures and distances in fundamental statistical pattern recognition problems. We then concisely review the main statistical distances and report a novel versatile family of divergences. Depending on their intrinsic complexity, the statistical patterns are learned by either atomic parametric distributions, semi-parametric finite mixtures, or non-parametricdoi:10.1007/978-3-642-39140-8_1 fatcat:g5cxsyc6q5gpdnme6ijoe7omuq