← Back to papers
2006 NIPS NeurIPS 2006

A PAC-Bayes Risk Bound for General Loss Functions

Abstract

We provide a PAC-Bayesian bound for the expected loss of convex combinations of classifiers under a wide class of loss functions (which includes the exponential loss and the logistic loss). Our numerical experiments with Adaboost indicate that the proposed upper bound, computed on the training set, behaves very similarly as the true loss estimated on the testing set.

🚀 Conference Pioneer — NIPS 2006
🧭 Keyword Pioneer — loss functions
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Deep Learning, Healthcare & Medicine, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
🐣 Hot Topic Early Bird — ensemble learning

Authors

Pascal Germain , Alexandre Lacasse , François Laviolette , Mario Marchand

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Bayesian Inference Machine Learning > Optimization & Theory > Learning Theory Machine Learning > Optimization & Theory > Statistical Learning Machine Learning > Bayesian & Probabilistic > Bayesian Learning

Keywords

ensemble learning classification convex optimization pac-bayesian bound pac-bayes bound risk bound loss function convex combination classifier ensemble
Download PDF

Related papers

Temporal Coding using the Response Properties of Spiking Neurons 2006
Parameter Expanded Variational Bayesian Methods 2006
Effects of Stress and Genotype on Meta-parameter Dynamics in Reinforcement Learning 2006
Ordinal Regression by Extended Binary Classification 2006
Blind source separation for over-determined delayed mixtures 2006