Landmark Papers in Machine Learning
This document attempts to collect the papers which developed important techniques in machine learning. Research is a collaborative process, discoveries are made independently, and the difference between the original version and a precursor can be subtle, but I’ve done my best to select the papers that I think are novel or significant.
My opinions are by no means the final word on these topics. Please create an issue or pull request if you have a suggestion.
| Icon | | | ---- | ------------------------------------------------------------ | | 🔒 | Paper behind paywall. In some cases, I provide an alternative link to the paper if it comes directly from one of the authors. | | 🔑 | Freely available version of paywalled paper, directly from the author. | | 💽 | Code associated with the paper. | | 🏛️ | Precursor or historically relevant paper. This may be a fundamental breakthrough that paved the way for the concept in question to be developed. | | 🔬 | Iteration, advancement, elaboration, or major popularization of a technique. | | 📔 | Blog post or something other than a formal publication. | | 🌐 | Website associated with the paper. | | 🎥 | Video associated with the paper. | | 📊 | Slides or images associated with the paper. |
Papers proceeded by “See also” indicate either additional historical context or else major developments, breakthroughs, or applications.
Mining Association Rules between Sets of Items in Large Databases (1993), Agrawal, Imielinski, and Swami, @CiteSeerX.
See also: The GUHA method of automatic hypotheses determination (1966), Hájek, Havel, and Chytil, @Springer 🔒 🏛️.
A Decision-Theoretic Generalization of on-Line Learning and an Application to Boosting (1997—published as abstract in 1995), Freund and Schapire, @CiteSeerX.
See also: Experiments with a New Boosting Algorithm (1996), Freund and Schapire, @CiteSeerX 🔬.