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Personalized Sleep Parameters Estimation from Actigraphy: A Machine Le NSS

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Background: The current gold standard for measuring sleep is polysomnography (PSG), but it can be obtrusive and costly. Actigraphy is a relatively low-cost and unobtrusive alternative to PSG. Of particular interest in measuring sleep from actigraphy is prediction of sleep-wake states. Current literature on prediction of sleep-wake states from actigraphy consists of methods that use population data, which we call generalized models. However, accounting for variability of sleep patterns across individuals calls for personalized models of sleep-wake states prediction that could be potentially better suited to individual-level data and yield more accurate estimation of sleep.


Machine learning: Harnessing the Power of Empirical Generalized Information (EGI)

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Part 2: Navigating the machine learning landscape -- supervised classifiers Supervised classifiers can sort items like posts to a discussion group or medical images, using one of many algorithms developed for the purpose.


Mastering Machine Learning With scikit-learn

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If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.


Mastering Machine Learning With scikit-learn

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If you are a software developer who wants to learn how machine learning models work and how to apply them effectively, this book is for you. Familiarity with machine learning fundamentals and Python will be helpful, but is not essential. This book examines machine learning models including logistic regression, decision trees, and support vector machines, and applies them to common problems such as categorizing documents and classifying images. It begins with the fundamentals of machine learning, introducing you to the supervised-unsupervised spectrum, the uses of training and test data, and evaluating models. You will learn how to use generalized linear models in regression problems, as well as solve problems with text and categorical features.


Community Blogs: Five AI and machine learning p... ServiceNow Community

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Machine learning becomes the new mobile: we founded Aeroprise in 1999 before smartphones or apps existed because it was obvious that the future of computing wasn't Clippy in a cube. By 2008, iOS and Android ushered in the era of "mobile first". Sundar Pichai officially ended that era with his pronouncement at the unveiling of an AI-powered Google Translate in November. Google, and eventually all tech stalwarts, will henceforward be "AI first." Public clouds make AI OAuth-simple: machine learning leaders Google, Microsoft, Amazon, Facebook, and Baidu have all released machine learning frameworks with APIs that make it as easy to add sentiment analysis or image recognition to apps as it was a few years ago to use OAuth to authenticate across platforms.