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2005 JMLR JMLR 2005

Learning with Decision Lists of Data-Dependent Features

Abstract

We present a learning algorithm for decision lists which allows features that are constructed from the data and allows a trade-off between accuracy and complexity. We provide bounds on the generalization error of this learning algorithm in terms of the number of errors and the size of the classifier it finds on the training data. We also compare its performance on some natural data sets with the set covering machine and the support vector machine. Furthermore, we show that the proposed bounds on the generalization error provide effective guides for model selection. [abs] [ pdf ][ bib ] © JMLR 2005. (edit, beta)

🧭 Keyword Pioneer — decision list rule
🐝 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, Speech & Audio
📈 Trend Setter — Feature Learning
🐣 Hot Topic Early Bird — feature learning

Authors

Mario Marchand , Marina Sokolova

Topics

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

Keywords

feature learning model selection generalization error support vector machine decision list decision list rule data-dependent feature
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