ASlib: A Benchmark Library for Algorithm Selection
Bernd Bischl,Pascal Kerschke,Lars Kotthoff,Marius Lindauer,Yuri Malitsky,Alexandre Fréchette,Holger H. Hoos,Frank Hutter,Kevin Leyton-Brown,Kevin Tierney,Joaquin Vanschoren +10 more
TL;DR: In this article, the authors introduce a standardized format for representing algorithm selection scenarios and a repository that contains a growing number of data sets from the literature, and demonstrate the potential of algorithm selection to achieve significant performance improvements across a broad range of problems and algorithms.
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About: This article is published in Artificial Intelligence. The article was published on 01 Aug 2016. and is currently open access. The article focuses on the topics: Weighted Majority Algorithm & Population-based incremental learning.
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References
•Journal Article
R: A language and environment for statistical computing.
TL;DR: Copyright (©) 1999–2012 R Foundation for Statistical Computing; permission is granted to make and distribute verbatim copies of this manual provided the copyright notice and permission notice are preserved on all copies.
410.8K
•Book
The Elements of Statistical Learning: Data Mining, Inference, and Prediction
Trevor Hastie,Robert Tibshirani,Jerome H. Friedman +2 more
- 28 Jul 2013
TL;DR: In this paper, the authors describe the important ideas in these areas in a common conceptual framework, and the emphasis is on concepts rather than mathematics, with a liberal use of color graphics.
21.3K
The WEKA data mining software: an update
TL;DR: This paper provides an introduction to the WEKA workbench, reviews the history of the project, and, in light of the recent 3.6 stable release, briefly discusses what has been added since the last stable version (Weka 3.4) released in 2003.
Classification and Regression by randomForest
Andy Liaw,Matthew C. Wiener +1 more
- 01 Jan 2007
TL;DR: random forests are proposed, which add an additional layer of randomness to bagging and are robust against overfitting, and the randomForest package provides an R interface to the Fortran programs by Breiman and Cutler.
Hierarchical Grouping to Optimize an Objective Function
TL;DR: In this paper, a procedure for forming hierarchical groups of mutually exclusive subsets, each of which has members that are maximally similar with respect to specified characteristics, is suggested for use in large-scale (n > 100) studies when a precise optimal solution for a specified number of groups is not practical.
19.8K