@inproceedings{2776f2f2703944e592677fbcd3ad4eee,
title = "CHIRP: A new classifier based on composite hypercubes on iterated random projections",
abstract = "We introduce a classifier based on the L∞ norm. This classifier, called CHIRP, is an iterative sequence of three stages (projecting, binning, and covering) that are designed to deal with the curse of dimensionality, computational complexity, and nonlinear separability. CHIRP is not a hybrid or modification of existing classifiers; it employs a new covering algorithm. The accuracy of CHIRP on widely-used benchmark datasets exceeds the accuracy of competitors. Its computational complexity is sub-linear in number of instances and number of variables and subquadratic in number of classes.",
keywords = "Random projections, Supervised classification",
author = "Leland Wilkinson and Anushka Anand and Tuan, {Dang Nhon}",
year = "2011",
doi = "10.1145/2020408.2020418",
language = "English",
isbn = "9781450308137",
series = "Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining",
pages = "6--14",
booktitle = "Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD'11",
note = "null ; Conference date: 21-08-2011 Through 24-08-2011",
}