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

Mónica Sánchez; Francesc Prats; Núria Agell; Xari Rovira

doi:10.1007/978-3-540-25945-9_41

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

Mónica Sánchez, Francesc Prats, Núria Agell, Xari Rovira

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

Resum

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.

Idioma original	Anglès
Títol de la publicació	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editors	Ricardo Conejo, Jose-Luis Perez-de-la-Cruz, Maite Urretavizcaya
Editor	Springer Verlag
Pàgines	415-424
Nombre de pàgines	10
ISBN (imprès)	3540222189, 9783540222187
DOIs	https://doi.org/10.1007/978-3-540-25945-9_41
Estat de la publicació	Publicada - 2004
Publicat externament	Sí
Esdeveniment	10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003 - San Sebastian, Spain Durada: 12 de nov. 2003 → 14 de nov. 2003

Sèrie de publicacions

Nom	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volum	3040
ISSN (imprès)	0302-9743
ISSN (electrònic)	1611-3349

Conferència

Conferència	10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003
País/Territori	Spain
Ciutat	San Sebastian
Període	12/11/03 → 14/11/03

Accés al document

10.1007/978-3-540-25945-9_41

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Sánchez, M., Prats, F., Agell, N., & Rovira, X. (2004). Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk. In R. Conejo, J.-L. Perez-de-la-Cruz, & M. Urretavizcaya (Ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pàg. 415-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3040). Springer Verlag. https://doi.org/10.1007/978-3-540-25945-9_41

Sánchez, Mónica ; Prats, Francesc ; Agell, Núria et al. / Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). editor / Ricardo Conejo ; Jose-Luis Perez-de-la-Cruz ; Maite Urretavizcaya. Springer Verlag, 2004. pàg. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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

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{\textquoteright} financial credit quality. A simple example that allows the kernel to be interpreted in terms of proximity of the patterns is presented.",

author = "M{\'o}nica S{\'a}nchez and Francesc Prats and N{\'u}ria Agell and Xari Rovira",

note = "Publisher Copyright: {\textcopyright} Springer-Verlag Berlin Heidelberg 2004.; 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003 ; Conference date: 12-11-2003 Through 14-11-2003",

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Sánchez, M, Prats, F, Agell, N & Rovira, X 2004, Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk. in R Conejo, J-L Perez-de-la-Cruz & M Urretavizcaya (ed.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 3040, Springer Verlag, pàg. 415-424, 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003, San Sebastian, Spain, 12/11/03. https://doi.org/10.1007/978-3-540-25945-9_41

Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk. / Sánchez, Mónica; Prats, Francesc; Agell, Núria et al.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). ed. / Ricardo Conejo; Jose-Luis Perez-de-la-Cruz; Maite Urretavizcaya. Springer Verlag, 2004. pàg. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3040).

Producció científica: Capítol de llibre › Contribució a congrés/conferència › Avaluat per experts

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N1 - Publisher Copyright: © Springer-Verlag Berlin Heidelberg 2004.

PY - 2004

Y1 - 2004

N2 - 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.

AB - 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.

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Sánchez M, Prats F, Agell N , Rovira X. Kernel functions over orders of magnitude spaces by means of usual kernels. Application to measure financial credit risk. In Conejo R, Perez-de-la-Cruz JL, Urretavizcaya M, editors, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. pàg. 415-424. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-540-25945-9_41

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

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