Error analysis of stochastic gradient descent ranking.
IEEE Trans Cybern
; 43(3): 898-909, 2013 Jun.
Article
em En
| MEDLINE
| ID: mdl-24083315
ABSTRACT
Ranking is always an important task in machine learning and information retrieval, e.g., collaborative filtering, recommender systems, drug discovery, etc. A kernel-based stochastic gradient descent algorithm with the least squares loss is proposed for ranking in this paper. The implementation of this algorithm is simple, and an expression of the solution is derived via a sampling operator and an integral operator. An explicit convergence rate for leaning a ranking function is given in terms of the suitable choices of the step size and the regularization parameter. The analysis technique used here is capacity independent and is novel in error analysis of ranking learning. Experimental results on real-world data have shown the effectiveness of the proposed algorithm in ranking tasks, which verifies the theoretical analysis in ranking error.
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Base de dados:
MEDLINE
Assunto principal:
Algoritmos
/
Reconhecimento Automatizado de Padrão
/
Inteligência Artificial
/
Processos Estocásticos
/
Modelos Estatísticos
/
Armazenamento e Recuperação da Informação
Tipo de estudo:
Risk_factors_studies
Idioma:
En
Ano de publicação:
2013
Tipo de documento:
Article