Variational relevance vector machine for tabular data

Dmitry Kropotov, Dmitry Vetrov, Lior Wolf, Tal Hassner

نتاج البحث: نشر في مجلةمقالة من مؤنمرمراجعة النظراء


We adopt the Relevance Vector Machine (RVM) framework to handle cases of tablestructured data such as image blocks and image descriptors. 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, as well as providing better interpretation of results. These properties are demonstrated on synthetic data-sets as well as on a modern and challenging visual identification benchmark.

اللغة الأصليةالإنجليزيّة
الصفحات (من إلى)79-94
عدد الصفحات16
دوريةJournal of Machine Learning Research
مستوى الصوت13
حالة النشرنُشِر - 2010
الحدث2nd Asian Conference on Machine Learning, ACML 2010 - Tokyo, اليابان
المدة: ٨ نوفمبر ٢٠١٠١٠ نوفمبر ٢٠١٠


أدرس بدقة موضوعات البحث “Variational relevance vector machine for tabular data'. فهما يشكلان معًا بصمة فريدة.

قم بذكر هذا