支持向量机的轻量级可视化工具

SeungJin Lim
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引用次数: 1

摘要

支持向量机(SVM)家族是当今最流行的监督学习算法之一。它在许多不同的应用领域以其出色的性能和准确性而闻名。在本文中,我们提出了一个可视化工具,以提高支持向量机的效用。它提供了访问每个数据点到最优超平面的距离度量。它还提供了距离值在特征空间中的分布。该工具还提供了平移、缩放和拾取的交互功能,这对检查错误分类的数据点很有用。该工具还集成了平移、缩放和拾取功能。这些交互特征有助于检测错误分类的数据点,从而提高支持向量机的性能。该工具被用于使用FT-IR光谱的癌症检测项目。光谱的可视化实现了噪声光谱的检测和去除。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Light-Weight Visualization Tool for Support Vector Machines
The family of Support Vector Machines (SVM) is one of the most popular supervised learning algorithms today. It is renowned for its outstanding performance and accuracy in many different application domains. In this paper, we present a visualization tool to enhance the utility of SVM. It provides access to the distance measure of each data point to the optimal hyperplane. It also provides the distribution of distance values in the feature space. %The tool also provides the interactive features of panning, zooming and picking, which are useful in inspecting misclassified data points. The tool also incorporates capabilities for panning, zooming, and picking. These interactive features are useful for inspecting misclassified data points to improve the performance of SVM. The tool was used in a cancer detection project using FT-IR spectra. Visualization of the spectra led to the detection and removal of noisy spectra.
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