PyLZJD: An Easy to Use Tool for Machine Learning

Simple item page
dc.contributor.authorRaff, Edward
dc.contributor.authorAurelio, Joe
dc.contributor.authorNicholas, Charles
dc.date.accessioned2019-10-04T14:27:21Z
dc.date.available2019-10-04T14:27:21Z
dc.descriptionTHE 18th PYTHON IN SCIENCE CONF. (SCIPY 2019)en_US
dc.description.abstractAs Machine Learning (ML) becomes more widely known and popular, so too does the desire for new users from other backgrounds to apply ML techniques to their own domains. A difficult prerequisite that often confounds new users is the feature creation and engineering process. This is especially true when users attempt to apply ML to domains that have not historically received attention from the ML community (e.g., outside of text, images, and audio). The Lempel Ziv Jaccard Distance (LZJD) is a compression based technique that can be used for many machine learning tasks. Because of its compression background, users do not need to specify any feature extraction, making it easy to apply to new domains. We introduce PyLZJD, a library that implements LZJD in a manner meant to be easy to use and apply for novice practitioners. We will discuss the intuition and high-level mechanics behind LZJD, followed by examples of how to use it on problems of disparate data typesen_US
dc.description.urihttp://conference.scipy.org/proceedings/scipy2019/pdfs/pylzjd.pdfen_US
dc.format.extent6 pagesen_US
dc.genreconference papers and proceedingsen_US
dc.identifierdoi:10.13016/m2t8rl-ccpp
dc.identifier.urihttp://hdl.handle.net/11603/14971
dc.language.isoen_USen_US
dc.relation.isAvailableAtThe University of Maryland, Baltimore County (UMBC)
dc.relation.ispartofUMBC Computer Science and Electrical Engineering Department Collection
dc.relation.ispartofUMBC Faculty Collection
dc.rightsThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.
dc.rightsAttribution 4.0 International (CC BY 4.0)*
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/*
dc.subjectcompressionen_US
dc.subjectcomplex dataen_US
dc.subjectmachine learningen_US
dc.titlePyLZJD: An Easy to Use Tool for Machine Learningen_US
dc.typeTexten_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
PyLZJD- An Easy to Use Tool for Machine Learning.pdf
Size:
985.4 KB
Format:
Adobe Portable Document Format
Description:
Download
License bundle
Now showing 1 - 1 of 1
No Thumbnail Available
Name:
license.txt
Size:
2.56 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection