Titlebook: Artificial Neural Networks in Pattern Recognition; 7th IAPR TC3 Worksho Friedhelm Schwenker,Hazem M. Abbas,Edmondo Trentin Conference proce

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Learning Sequential Data with the Help of Linear Systemsty of the problem to face, linear dynamical systems may directly contribute to provide a good solution at a reduced computational cost, or indirectly provide support at a pre-training stage for nonlinear models. We present and discuss several approaches, both linear and nonlinear, where linear dynam

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Incremental Construction of Low-Dimensional Data Representationsperties of the initial data. Typically, such algorithms use the solution of large-dimensional optimization problems, and the incremental versions are designed for many popular algorithms to reduce their computational complexity. Under manifold assumption about high-dimensional data, advanced manifol

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AROMA

Co-training with Credal Models convex probability sets, they can select multiple classes as prediction when information is insufficient and predict a unique class only when the available information is rich enough. The goal of this paper is to explore whether this particular feature can be used advantageously in the setting of c

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Interpretable Classifiers in Precision Medicine: Feature Selection and Multi-class Categorizationnd treatments this change also alters the diagnostic task from binary to multi-categorial decisions. Keeping the corresponding multi-class architectures accurate and interpretable is currently one of the key tasks in molecular diagnostics..In this work, we specifically address the question to which