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首页> 外文期刊>Neural Networks and Learning Systems, IEEE Transactions on >Network-Based Stochastic Semisupervised Learning
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Network-Based Stochastic Semisupervised Learning

机译：基于网络的随机半监督学习

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摘要

Semisupervised learning is a machine learning approach that is able to employ both labeled and unlabeled samples in the training process. In this paper, we propose a semisupervised data classification model based on a combined random-preferential walk of particles in a network (graph) constructed from the input dataset. The particles of the same class cooperate among themselves, while the particles of different classes compete with each other to propagate class labels to the whole network. A rigorous model definition is provided via a nonlinear stochastic dynamical system and a mathematical analysis of its behavior is carried out. A numerical validation presented in this paper confirms the theoretical predictions. An interesting feature brought by the competitive-cooperative mechanism is that the proposed model can achieve good classification rates while exhibiting low computational complexity order in comparison to other network-based semisupervised algorithms. Computer simulations conducted on synthetic and real-world datasets reveal the effectiveness of the model.
机译：半监督学习是一种机器学习方法，能够在训练过程中同时使用标记和未标记的样本。在本文中，我们提出了一个半监督数据分类模型，该模型基于从输入数据集构建的网络（图形）中粒子的随机-优先组合游动。相同类别的粒子相互协作，而不同类别的粒子相互竞争以将类别标签传播到整个网络。通过非线性随机动力学系统提供了严格的模型定义，并对其行为进行了数学分析。本文提出的数值验证证实了理论预测。竞争合作机制带来的一个有趣特征是，与其他基于网络的半监督算法相比，所提出的模型可以实现良好的分类率，同时显示出较低的计算复杂度顺序。在合成和真实数据集上进行的计算机仿真揭示了该模型的有效性。

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