An Investigation of Transfer Learning and Traditional Machine Learning Algorithms

Karl R. Weiss, T. Khoshgoftaar
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引用次数: 10

Abstract

Previous research focusing on the evaluation of transfer learning algorithms has predominantly used real-world datasets to measure an algorithm's performance. A test with a real-world dataset exposes an algorithm to a single instance of distribution difference between the training (source) and test (target) datasets. These previous works have not measured performance over a wide-range of source and target distribution differences. We propose to use a test framework that creates many source and target datasets from a single base dataset, representing a diverse-range of distribution differences. These datasets will be used as a stress test to measure an algorithm's performance. The stress test process will measure and compare different transfer learning algorithms and traditional learning algorithms. The unique contributions of this paper, with respect to transfer learning, are defining a test framework, defining multiple distortion profiles, defining a stress test suite, and the evaluation and comparison of different transfer learning and traditional machine learning algorithms over a wide-range of distributions.
迁移学习与传统机器学习算法研究
以前的研究主要集中在迁移学习算法的评估上,主要使用真实世界的数据集来衡量算法的性能。使用真实数据集的测试将算法暴露给训练(源)和测试(目标)数据集之间分布差异的单个实例。这些先前的工作没有在大范围内测量源和目标分布差异的性能。我们建议使用一个测试框架,从单个基本数据集创建许多源数据集和目标数据集,代表不同范围的分布差异。这些数据集将用作压力测试来衡量算法的性能。压力测试过程将测量和比较不同的迁移学习算法和传统学习算法。在迁移学习方面,本文的独特贡献在于定义了一个测试框架,定义了多个失真曲线,定义了一个压力测试套件,并在广泛的分布范围内评估和比较了不同的迁移学习和传统机器学习算法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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