An unsupervised approach to feature discretization and selection
Many learning problems require handling high dimensional data sets with a relatively small number of instances. Learning algorithms are thus confronted with the curse of dimensionality, and need to address it in order to be effective. Examples of these types of data include the bag-of-words represen...
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| Formato: | article |
| Idioma: | eng |
| Publicado em: |
2018
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| Texto completo: | http://hdl.handle.net/10400.21/8569 |
| País: | Portugal |
| Oai: | oai:repositorio.ipl.pt:10400.21/8569 |