Multiclass relevance vector machines: sparsity and accuracy.
IEEE Trans Neural Netw
; 21(10): 1588-98, 2010 Oct.
Article
em En
| MEDLINE
| ID: mdl-20805053
ABSTRACT
In this paper, we investigate the sparsity and recognition capabilities of two approximate Bayesian classification algorithms, the multiclass multi-kernel relevance vector machines (mRVMs) that have been recently proposed. We provide an insight into the behavior of the mRVM models by performing a wide experimentation on a large range of real-world datasets. Furthermore, we monitor various model fitting characteristics that identify the predictive nature of the proposed methods and compare against existing classification techniques. By introducing novel convergence measures, sample selection strategies and model improvements, it is demonstrated that mRVMs can produce state-of-the-art results on multiclass discrimination problems. In addition, this is achieved by utilizing only a very small fraction of the available observation data.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Inteligência Artificial
/
Teorema de Bayes
/
Biologia Computacional
Tipo de estudo:
Prognostic_studies
Idioma:
En
Ano de publicação:
2010
Tipo de documento:
Article