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首页> 外文会议>International Computer Conference, Computer Society of Iran >Subspace Learning Augmented with Class Conditional Probability Estimation Based on SVM Classifier in Domain Adaptation
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Subspace Learning Augmented with Class Conditional Probability Estimation Based on SVM Classifier in Domain Adaptation

机译：子空间学习基于SVM分类器的阶级条件概率估计在域自适应中

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摘要

The rapid evolution of data has challenged traditional machine learning methods and leads to the failure of many learning models. As a possible solution to the lack of sufficient labeled data, transfer learning aims to exploit the accumulated knowledge in an auxiliary domain to develop new predictive models. This article studies a specific type of transfer learning called domain adaptation, which works based on subspace learning in order to minimize distance between class conditional probability distributions of source and target domains and to preserve source discriminative information. SVM classifier trained on source domain data has been used to predict target domain data labels to facilitate subspace learning. In this work, subspace learning is formulated as an optimization problem and experiments have been carried out on the real- world datasets. The results of experiments indicate that the proposed method outperforms several exiting methods at this field in the term of accuracy in two object recognition benchmarks: Offlce-Caltech10 and Office datasets.
机译：数据的快速发展提出了挑战传统的机器学习方法，并导致许多学习模式的失败。作为一个可能的解决方案缺乏足够的标签数据，迁移学习旨在利用在辅助领域积累的知识来开发新的预测模型。本文研究转移学习的具体类型，称为领域适应性，其工作原理基于子空间学习，以减少源和目标域的类条件概率分布之间的距离，并保留源判别信息。训练有素的源域数据SVM分类已被用来预测目标域数据标签，以方便子空间学习。在这项工作中，子空间学习配制成最优化问题和实验已经对现实世界的数据集进行。实验结果表明，该方法优于几种退出方式，在这一领域中的精度两个物体识别基准测试术语：Offlce-Caltech10和Office的数据集。

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