Titlebook: Machine Learning in Radiation Oncology; Theory and Applicati Issam El Naqa,Ruijiang Li,Martin J. Murphy Book 20151st edition Springer Inter

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Harbor

What Is Machine Learning?vironment. They are considered the working horse in the new era of the so-called big data. Techniques based on machine learning have been applied successfully in diverse fields ranging from pattern recognition, computer vision, spacecraft engineering, finance, entertainment, and computational biolog

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Computational Learning Theoryappropriate learning algorithm for a particular task. In this chapter, we present the two main theoretical frameworks—probably approximately correct (PAC) and Vapnik–Chervonenkis (VC) dimension—which allow us to answer questions such as which learning process we should select, what is the learning c

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Ptsd429

水槽

Informatics in Radiation Oncologyuestions and an abundance of data, machine learning technologies can be valuable. Available data includes handwritten notes on paper, imaging data available in digital formats, radiation treatment plan details, financial data, and multilevel multicenter databases, to name a few. Tools of various com

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Application of Machine Learning for Multicenter Learning treatment options, as more information is needed to make an informed decision. One of the methods is to use machine-learning techniques to develop predictive models. Although prediction models, embedded in clinical decision support systems (CDSSs), are the foreseen solution, developing/training suc

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Mechanics