Classification in the multiple instance learning framework via spherical separation

被引：18

作者：

Gaudioso, M. ^{[1
]}

Giallombardo, G. ^{[1
]}

Miglionico, G. ^{[1
]}

Vocaturo, E. ^{[1
]}

机构：

[1] Univ Calabria, DIMES, Arcavacata Di Rende, Italy

来源：

SOFT COMPUTING | 2020年 / 24卷 / 07期

关键词：

Classification; Multiple instance learning; Spherical separation; DC functions; Bundle methods; BUNDLE METHOD; DC;

D O I：

10.1007/s00500-019-04255-1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

We consider a multiple instance learning problem where the objective is the binary classifications of bags of instances, instead of single ones. We adopt spherical separation as a classification tool and come out with an optimization model which is of difference-of-convex type. We tackle the model by resorting to a specialized nonsmooth optimization algorithm, recently proposed in the literature which is based on objective function linearization and bundling. The results obtained by applying the proposed approach to some benchmark test problems are also reported.

引用

页码：5071 / 5077

页数：7

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