人机协同的机器学习_Human-in-the-Loop_Machine_Learning.pdf

简介：
本论文探讨了人机协作在机器学习领域的应用，通过结合人类知识与算法优化，提升模型性能和效率。文档深入分析了Human-in-the-Loop方法的优势、挑战及未来发展方向。 Human-in-the-Loop Machine Learning explores methods for effective collaboration between humans and machines. Most machine learning systems currently in use learn from human feedback, yet the majority of machine learning courses concentrate almost exclusively on algorithms rather than the human-computer interaction aspects of these systems. This can create a significant knowledge gap for data scientists working with real-world applications, where they often spend more time managing data than developing algorithms. Human-in-the-Loop Machine Learning is a practical guide that aims to optimize every aspect of the machine learning process. It covers techniques such as annotation, active learning, transfer learning, and utilizing machine learning to enhance each step of the workflow. The book emphasizes human feedbacks role in improving application performance by ensuring higher model accuracy, reducing data errors, lowering costs, and accelerating deployment. Human-in-the-Loop Machine Learning provides best practices for selecting sample data for human input, quality control measures for annotations, and designing efficient annotation interfaces. It also covers creating training datasets for various tasks such as labeling, object detection, semantic segmentation, sequence labeling, among others. The book begins with foundational concepts and gradually delves into advanced techniques like transfer learning and self-supervised learning within the context of annotation workflows. The author Robert (Munro) Monarch is a data scientist and engineer who has extensive experience in building machine learning datasets for various companies.