A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

Avolio Matteo; Fuduli Antonio

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A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

机译：用于二进制多实例学习的半辅助支持向量机方法

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We face a binary multiple instance learning (MIL) problem, whose objective is to discriminate between two kinds of point sets: positive and negative. In the MIL terminology, such sets are called bags, and the points inside each bag are called instances. Considering the case with two classes of instances (positive and negative) and inspired by a well-established instance-space support vector machine (SVM) model, we propose to extend to MIL classification the proximal SVM (PSVM) technique that has revealed very effective for supervised learning, especially in terms of computational time. In particular, our approach is based on a new instance-space model that exploits the benefits coming from both SVM (better accuracy) and PSVM (computational efficiency) paradigms. Starting from the standard MIL assumption, such a model is aimed at generating a hyperplane placed in the middle between two parallel hyperplanes: the first one is a proximal hyperplane that clusters the instances of the positive bags, while the second one constitutes a supporting hyperplane for the instances of the negative bags. Numerical results are presented on a set of MIL test data sets drawn from the literature.

机译：我们面临二进制多实例学习（MIL）问题，其目的是区分两种点集：正负。在MIL术语中，这样的组称为袋子，每个袋子内的点被称为实例。考虑到两类实例（正负）和受到良好的实例 - 空间支持向量机（SVM）模型的启发的情况，我们建议扩展到MIL分类近似SVM（PSVM）技术，揭示了非常有效的对于监督学习，特别是在计算时间方面。特别是，我们的方法基于新的实例 - 空间模型，用于利用SVM（更好的精度）和PSVM（计算效率）范例的益处。从标准密耳的假设开始，这种模型旨在生成放置在两个并行超平面之间的中间的超平面：第一个是近似超平面，围绕正袋的情况，而第二个是构成支持的超平面负袋的情况。在从文献中绘制的一组MIL测试数据集上呈现了数值结果。

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来源
《Neural Networks and Learning Systems, IEEE Transactions on》 |2021年第8期|3566-3577|共12页
作者
Avolio Matteo; Fuduli Antonio;
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作者单位

Univ Calabria Dept Math & Comp Sci I-87036 Arcavacata Di Rende Italy;

Univ Calabria Dept Math & Comp Sci I-87036 Arcavacata Di Rende Italy;

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原文格式 PDF
正文语种 eng
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关键词
Support vector machines; Standards; Computational modeling; Minimization; Indexes; Supervised learning; Multiple instance learning (MIL); proximal support vector machine (PSVM); semiproximal support vector machine; support vector machine (SVM);

机译：支持矢量机;标准;计算建模;最小化;指数;监督学习;多实例学习（MIL）;近端支持向量机（PSVM）;半偏移支持向量机;支持矢量机（SVM）;

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2. Classification of the Priority of Auditing XBRL Instance Documents with Fuzzy Support Vector Machines Algorithm [J] . Guang Yih Sheu 自主智能(英文) . 2019,第002期
3. A novel machine learning approach to the detection of identity theft in social networks based on emulated attack instances and support vector machines [J] . Villar‐Rodríguez E., Del Ser J., Torre‐Bastida A. I., Concurrency and computation: practice and experience . 2016,第4期

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6. A Multiple Instance Learning Approach for Content Based Image Retrieval Using One-Class Support Vector Machine [C] . Chengcui Zhang, Xin Chen, Min Chen, International broadcasting convention;IBC1990 . 1990

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7. Geometric Approach to Support Vector Machines Learning for Large Datasets [D] . Strack, Robert 2013

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机译：利用超高分辨率图像绘制世界贫民窟的高斯多实例学习方法。

A Semiproximal Support Vector Machine Approach for Binary Multiple Instance Learning

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