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

Producción científica: Capítulo del libro › Contribución a congreso/conferencia › revisión exhaustiva

Resumen

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	Inglés
Título de la publicación alojada	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Editores	Ricardo Conejo, Jose-Luis Perez-de-la-Cruz, Maite Urretavizcaya
Editorial	Springer Verlag
Páginas	415-424
Número de páginas	10
ISBN (versión impresa)	3540222189, 9783540222187
DOI	https://doi.org/10.1007/978-3-540-25945-9_41
Estado	Publicada - 2004
Publicado de forma externa	Sí
Evento	10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003 - San Sebastian, Espana Duración: 12 nov 2003 → 14 nov 2003

Serie de la publicación

Nombre	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen	3040
ISSN (versión impresa)	0302-9743
ISSN (versión digital)	1611-3349

Conferencia

Conferencia	10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003
País/Territorio	Espana
Ciudad	San Sebastian
Período	12/11/03 → 14/11/03

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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. En R. Conejo, J-L. Perez-de-la-Cruz, & M. Urretavizcaya (Eds.), Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 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. pp. 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",

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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. En R Conejo, J-L Perez-de-la-Cruz & M Urretavizcaya (eds.), 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, pp. 415-424, 10th Conference of the Spanish Association for Artificial Intelligence, CAEPIA 2003 and 5th Conference on Technology Transfer, TTIA 2003, San Sebastian, Espana, 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. p. 415-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 3040).

Producción científica: Capítulo del libro › Contribución a congreso/conferencia › revisión exhaustiva

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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. En Conejo R, Perez-de-la-Cruz J-L, Urretavizcaya M, editores, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag. 2004. p. 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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