IEEE Trans Neural Netw Learn Syst - Incremental Support Vector Learning for Ordinal Regression.

Tópicos

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{ learn(2355) train(1041) set(1003) }
{ result(1111) use(1088) new(759) }
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Resumo

Support vector ordinal regression (SVOR) is a popular method to tackle ordinal regression problems. However, until now there were no effective algorithms proposed to address incremental SVOR learning due to the complicated formulations of SVOR. Recently, an interesting accurate on-line algorithm was proposed for training -support vector classification (-SVC), which can handle a quadratic formulation with a pair of equality constraints. In this paper, we first present a modified SVOR formulation based on a sum-of-margins strategy. The formulation has multiple constraints, and each constraint includes a mixture of an equality and an inequality. Then, we extend the accurate on-line -SVC algorithm to the modified formulation, and propose an effective incremental SVOR algorithm. The algorithm can handle a quadratic formulation with multiple constraints, where each constraint is constituted of an equality and an inequality. More importantly, it tackles the conflicts between the equality and inequality constraints. We also provide the finite convergence analysis for the algorithm. Numerical experiments on the several benchmark and real-world data sets show that the incremental algorithm can converge to the optimal solution in a finite number of steps, and is faster than the existing batch and incremental SVOR algorithms. Meanwhile, the modified formulation has better accuracy than the existing incremental SVOR algorithm, and is as accurate as the sum-of-margins based formulation of Shashua and Levin.

Resumo Limpo

support vector ordin regress svor popular method tackl ordin regress problem howev now effect algorithm propos address increment svor learn due complic formul svor recent interest accur onlin algorithm propos train support vector classif svc can handl quadrat formul pair equal constraint paper first present modifi svor formul base sumofmargin strategi formul multipl constraint constraint includ mixtur equal inequ extend accur onlin svc algorithm modifi formul propos effect increment svor algorithm algorithm can handl quadrat formul multipl constraint constraint constitut equal inequ import tackl conflict equal inequ constraint also provid finit converg analysi algorithm numer experi sever benchmark realworld data set show increment algorithm can converg optim solut finit number step faster exist batch increment svor algorithm meanwhil modifi formul better accuraci exist increment svor algorithm accur sumofmargin base formul shashua levin

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