← Back to papers
2008 JMLR JMLR 2008

An Error Bound Based on a Worst Likely Assignment

Abstract

This paper introduces a new PAC transductive error bound for classification. The method uses information from the training examples and inputs of working examples to develop a set of likely assignments to outputs of the working examples. A likely assignment with maximum error determines the bound. The method is very effective for small data sets. [abs] [ pdf ][ bib ] © JMLR 2008. (edit, beta)

🐣 Hot Topic Early Bird — pac learning
🐝 Cross-Pollinator — Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Security & Privacy, Speech & Audio

Authors

Eric Bax , Augusto Callejas

Topics

Machine Learning > Core Methods > Classification Machine Learning > Optimization & Theory > Statistical Learning

Keywords

transductive learning pac learning classification error error bound
Download PDF

Related papers

On the Equivalence of Linear Dimensionality-Reducing Transformations 2008
Near-Optimal Sensor Placements in Gaussian Processes: Theory, Efficient Algorithms and Empirical Studies 2008
Algorithms for Sparse Linear Classifiers in the Massive Data Setting 2008
Graphical Methods for Efficient Likelihood Inference in Gaussian Covariance Models 2008
Shark 2008