Detalhes do Documento

Trading off Distance Metrics vs Accuracy in Incremental Learning Algorithms

Autor(es): Lopes, Noel ; Ribeiro, Bernardete

Data: 2018

Identificador Persistente: http://hdl.handle.net/10314/3951

Origem: Repositório Científico da Universidade Politécnica da Guarda

Assunto(s): Distance metrics, Instance-based learning, Nearest Neigh- bor, Incremental learning, Incremental Hypersphere Classi er (IHC)


Descrição

With the growth and development of data, the empirical evidence supporting a link between the distance metrics that are used in the instance-based algorithms and generalization has been mounting. In this paper, we look at distinct similarity measures to study its impact on the performance accuracy of incremental instance-based algorithms in pattern recognition problems. An in-depth analysis of the results of the proposed study for a variety of classi cation tasks (binary and multi-way) from various di erent domains shines light on the trade o between the distance metrics and yielded accuracy.

Tipo de Documento Livro
Idioma Inglês
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