Novel Fisher Subspace based Classifiers - Rozza, Alessandro
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In this book we present novel classifiers based on a new approach to estimate the Fisher Subspace. We face different classification tasks with the aim to stress our methods, to demonstrate their quality, and to show that these classifiers solve the following problems: 1. Manage unbalanced classes; 2. Solve the small sample size problem; 3. Deal with high dimensional data; 4. Manage classification tasks where the space dimensionality is approximately equal to the cardinality of the training set; 5. Relax the linear separability constraint reducing the overfitting problems.

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Produktbeschreibung
In this book we present novel classifiers based on a new approach to estimate the Fisher Subspace. We face different classification tasks with the aim to stress our methods, to demonstrate their quality, and to show that these classifiers solve the following problems: 1. Manage unbalanced classes; 2. Solve the small sample size problem; 3. Deal with high dimensional data; 4. Manage classification tasks where the space dimensionality is approximately equal to the cardinality of the training set; 5. Relax the linear separability constraint reducing the overfitting problems.
Autorenporträt
Rozza obtained his Master degree cum Laude, from the Department of Computer Science of Università degli Studi di Milano-Bicocca in the 2006. He received the Ph.D. degree in Computer Science in March 2011 from the Department of Scienze dell¿Informazione, Università degli Studi di Milano. His research interests are in the machine learning field.