Among the main problems are a well-known lack of data in general and representative data in particular, an overall inability to label data at scale, unknown data quality due to differences in data collection strategies, and data privacy issues that are unique to network data.
Aug 14, 2020 · Machine learning can actually amplify bias, and you can never be done checking for bias. The combination of a lack of training labels—with both ...
Aug 14, 2020 · Machine learning can actually amplify bias, and you can never be done checking for bias. The combination of a lack of training labels—with both ...
Features of bad data? Difficulty in labeling at scale. Challenges in using ML in networking. Lack of agreement in community. Sharing raw ...
Challenges in Using ML for Networking Research - ResearchGate
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Supervised and unsupervised learning-based methods are the most applied ML techniques for traffic classification in the networked systems [15].
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Posted: Aug 18, 2020
Posted: Aug 18, 2020
Among the key challenges are a commonly-acknowledged and much-maligned lack of readily available data, questions concerning the representativeness of collected ...
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Jun 18, 2018 · Having conflicting labels can be thought of as having noise or randomness in your labeling scheme. This can be done deliberately in an attempt to improve ...
May 31, 2024 · This article explores various labeling strategies that can help you efficiently label your data and unlock the full potential of your machine learning models.
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This guide will teach you everything you need to know about data labeling. You'll learn about technology, terminology, best practices, and the top questions to ...