元启发式算法在医疗应用中的方法与应用
Metaheuristics Algorithms for Medical Applications: Methods and Applicationsprovides readers with the most complete reference for developing Metaheuristics techniques with Machine Learning for solving biomedical problems. The book is organized to present a stepwise progression beginning with the basics of Metaheuristics, leading into methods and practices, and concluding with advanced topics. The first section of the book presents the fundamental concepts of Metaheuristics and Machine Learning, and also provides a comprehensive taxonomic view of Metaheuristics methods according to a variety of criteria such as data type, scope, method, and so forth. The second section of the book explains how to apply Metaheuristics techniques for solving large-scale biomedical problems, including analysis and validation under different strategies. The final portion of the book focuses on advanced topics in Metaheuristics in four different applications. Readers will discover a variety of new methods, approaches, and techniques, as well as a wide range of applications demonstrating key concepts in Metaheuristics for biomedical science. The book provides a leading-edge resource for researchers in a variety of scientific fields who are interested in metaheuristics, including mathematics, biomedical engineering, computer science, biological sciences, and clinicians in medical practice.
《元启发式算法在医疗应用中的方法与应用》为读者提供了开发使用机器学习解决生物医学问题的元启发式技术的最佳参考书。本书按步骤进行组织，从元启发式的最基本概念开始介绍，再到方法和实践，并最终转向高级主题。书中第一部分介绍了元启发式和机器学习的基本概念，并根据数据类型、范围、方法等多种标准提供了一个综合性的元启发式方法分类视图。第二部分解释了如何使用元启发式技术解决大规模生物医学问题的方法，包括在不同策略下进行分析和验证的过程。本书的最后一部分专注于四个不同的应用领域中的高级元启发式主题。读者将发现各种新的方法、技术和方法，以及广泛的生物医学应用来展示元启发式的关键概念。这本书为数学、生物医学工程、计算机科学、生命科学等领域感兴趣于元启发式的科研人员提供了领先资源，包括临床医生在医疗实践中。 《元启发式算法在医疗应用中的方法与应用》提供读者最全面的参考资料，用于开发使用机器学习解决生物医学问题的元启发式技术。本书按步骤呈现，从元启发式的基本概念开始介绍，到方法和实践，并最终转向高级主题。第一部分展示元启发式的根本概念以及按照数据类型、范围、方法等多种标准提供的综合性元启发式方法分类视图。第二部分解释如何使用元启发式技术解决大规模生物医学问题的方法，在不同的策略下进行分析和验证。最后一部分专注于四个应用领域的高级元启发式主题，读者将发现各种新的方法和技术以及广泛的生物医学应用来展示元启发式的概念在生物医学科学中的重要性。本书为数学、生物医学工程、计算机科学、生命科学等领域内的研究人员提供了领先资源，包括临床医生在内的医疗实践者。
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