Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk

Mónica Sánchez, Francesc Prats, N. Agell, X. Rovira Llobera

Research output: Book chapterConference contributionpeer-review

Abstract

This paper lies within the domain of learning algorithms based on kernel functions, as in the case of Support Vector Machines. These algorithms provide good results in classification problems where the input data are not linearly separable. A kernel is constructed over the discrete structure of absolute orders of magnitude spaces. This kernel will be applied to measure firms’ financial credit quality. A simple example that allows the kernel to be interpreted in terms of proximity of the patterns is presented.

Original languageEnglish
Title of host publicationLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
EditorsRicardo Conejo, Jose-Luis Perez-de-la-Cruz, Maite Urretavizcaya
PublisherSpringer Verlag
Pages415-424
Number of pages10
ISBN (Print)3540222189, 9783540222187
DOIs
Publication statusPublished - 2004
Externally publishedYes
Event10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003 - San Sebastian, Spain
Duration: 12 Nov 200314 Nov 2003

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume3040
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003
Country/TerritorySpain
CitySan Sebastian
Period12/11/0314/11/03

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