Cargando…
A Novel Boolean Kernels Family for Categorical Data †
Kernel based classifiers, such as SVM, are considered state-of-the-art algorithms and are widely used on many classification tasks. However, this kind of methods are hardly interpretable and for this reason they are often considered as black-box models. In this paper, we propose a new family of Bool...
Autores principales: | Polato, Mirko, Lauriola, Ivano, Aiolli, Fabio |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512961/ https://www.ncbi.nlm.nih.gov/pubmed/33265534 http://dx.doi.org/10.3390/e20060444 |
Ejemplares similares
-
Propositional Kernels
por: Polato, Mirko, et al.
Publicado: (2021) -
Learning adaptive representations for entity recognition in the biomedical domain
por: Lauriola, Ivano, et al.
Publicado: (2021) -
Scuba: scalable kernel-based gene prioritization
por: Zampieri, Guido, et al.
Publicado: (2018) -
HIV drug resistance prediction with weighted categorical kernel functions
por: Ramon, Elies, et al.
Publicado: (2019) -
A Novel Data-Driven Boolean Model for Genetic Regulatory Networks
por: Chen, Leshi, et al.
Publicado: (2018)