利用共轭矩阵和正则化解决弱控制回归问题

L. Cherikbayeva, N. Mukazhanov, Z. Alibiyeva, S. Adilzhanova, G. Tyulepberdinova, M. Sakypbekova
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引用次数: 0

摘要

目前,机器学习(ML)的理论和方法正在迅速发展,并越来越多地应用于各个科技领域,特别是制造、教育和医疗领域。弱监督学习是机器学习研究的一个分支,旨在开发用于分析各类信息的模型和方法。在提出弱监督学习问题时,假定模型中的某些对象没有被正确定义。这种不准确可以从不同的角度来理解。弱监督学习是一种机器学习方法,它使用不完整、不准确或不精确的观测信号来训练模型,而不是使用完全有效的数据。出于各种原因,弱监督学习经常出现在现实世界的问题中。这可能是由于数据标注过程成本高、传感器精度低、缺乏专家经验或人为错误。例如,在通过众包方法获得的情况下,对控制不佳的物体进行标注:对于每个物体都有一组不同的评估,其质量取决于执行者的技能。另一个例子是图像中的物体检测问题。在物体检测任务中,边界线是指示图像中检测到的物体的位置和大小的常用方法。文章介绍了一种利用瓦瑟斯坦度量、各种正则化和共同关联矩阵作为相似性矩阵来解决多目标弱监督回归问题的算法。这项工作还改进了计算加权平均共关联矩阵的算法。我们将提出的算法与现有的监督学习算法和无监督学习算法在合成数据和真实数据上进行了比较。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SOLUTION TO THE PROBLEM WEAKLY CONTROLLED REGRESSION USING COASSOCIATION MATRIX AND REGULARIZATION
Currently, the theory and methods of machine learning (ML) are rapidly developing and are increasingly used in various fields of science and technology, in particular in manufacturing, education and medicine. Weakly supervised learning is a subset of machine learning research that aims to develop models and methods for analyzing various types of information. When formulating a weakly supervised learning problem, it is assumed that some objects in the model are not defined correctly. This inaccuracy can be understood in different ways. Weakly supervised learning is a type of machine learning method in which a model is trained using incomplete, inaccurate, or imprecise observation signals rather than using fully validated data. Weakly supervised learning often occurs in real-world problems for various reasons. This may be due to the high cost of the data labeling process, low sensor accuracy, lack of expert experience, or human error. For example, labeling of poor control is carried out in cases obtained by crowdsourcing methods: for each object there is a set of different assessments, the quality of which depends on the skill of the performers. Another example is the problem of object detection in an image. Boundary lines are a common way to indicate the location and size of objects detected in an image in object detection tasks. The article presents an algorithm for solving a multi-objective weakly supervised regression problem using the Wasserstein metric, various regularizations and a co-association matrix as a similarity matrix. The work also improved the algorithm for calculating the weighted average co-association matrix. We compare the proposed algorithm with existing supervised learning and unsupervised learning algorithms on synthetic and real data.
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