A Bayesian Boosting Model

09/10/2012

∙

We offer a novel view of AdaBoost in a statistical setting. We propose a Bayesian model for binary classification in which label noise is modeled hierarchically. Using variational inference to optimize a dynamic evidence lower bound, we derive a new boosting-like algorithm called VIBoost. We show its close connections to AdaBoost and give experimental results from four datasets.

READ FULL TEXT

A Bayesian Boosting Model

Stochastic Bayesian Neural Networks

Boosting Variational Inference: an Optimization Perspective

Distributed Variational Inference for Online Supervised Learning

Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach

On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models

Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting

Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning

A Bayesian Boosting Model

Related Research

Stochastic Bayesian Neural Networks

Boosting Variational Inference: an Optimization Perspective

Distributed Variational Inference for Online Supervised Learning

Classification of fNIRS Data Under Uncertainty: A Bayesian Neural Network Approach

On the Convergence of Extended Variational Inference for Non-Gaussian Statistical Models

Statistical Guarantees and Algorithmic Convergence Issues of Variational Boosting

Local Adaptivity of Gradient Boosting in Histogram Transform Ensemble Learning