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Bayesian Logistic Regression for Classification of Tabular Dataстатья
- Авторы: Kropotov D., Vetrov D., Wolf L., Hassner T.
- Сборник: Proc. 8th International Conference "Intelligent Information Processing", Cyprus
- Год издания: 2010
- Место издания: Maks Press Moscow
- Первая страница: 103
- Последняя страница: 106
- Аннотация: We extend the Relevance Vector Machine (RVM) framework to handle cases of table-structured data, i. e. when each object is represented by a table of features rather than by a single vector. This is achieved by coupling the regularization coefficients of rows and columns of features. We present two variants of this new gridRVM framework, based on the way in which the regularization coefficients of the rows and columns are combined. Appropriate variational optimization algorithms are derived for inference within this framework. The consequent reduction in the number of parameters from the product of the table’s dimensions to the sum of its dimensions allows for better performance in the face of small training sets, resulting in improved resistance to overfitting problems, as well as providing better interpretation of results. These properties are demonstrated on a synthetic data-set as well as on a modern and challenging visual identification benchmark.
- Добавил в систему: Кропотов Дмитрий Александрович