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About RVM Tutorial
This document provides a tutorial on relevance vector machines (RVM) for regression and classification. It begins with an introduction to linear models and Bayesian approaches for regression. It then describes the theory behind RVM, which uses a sparse Bayesian prior to achieve model sparseness. This results in a student-t prior on the weight parameters, leading to automatic relevance determination. The document outlines the Bayesian inference procedure for RVM, including approximating the parameter posterior distribution. It also describes optimizing the marginal likelihood to determine the hyperparameters. Two examples applying RVM for object detection and microcalcification classification from images are then discussed.
Detailed Information
| Author: | ['rcoca_1'] |
|---|---|
| Publication Year: | 2015 |
| Pages: | 25 |
| Language: | English |
| Format: | |
| Price: | FREE |
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