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Titlebook: Applied Machine Learning; David Forsyth Textbook 2019 Springer Nature Switzerland AG 2019 machine learning.naive bayes.nearest neighbor.SV

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发表于 2025-3-21 18:30:57 | 显示全部楼层 |阅读模式
期刊全称Applied Machine Learning
影响因子2023David Forsyth
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发行地址Covers the ideas in machine learning that everyone going to use learning tools should know, whatever their chosen specialty or career.Broad coverage of the area ensures enough to get the reader starte
图书封面Titlebook: Applied Machine Learning;  David Forsyth Textbook 2019 Springer Nature Switzerland AG 2019 machine learning.naive bayes.nearest neighbor.SV
影响因子Machine learning methods are now an important tool for scientists, researchers, engineers and students in a wide range of areas.  This book is written for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learning toolkit. .Applied Machine Learning. covers many topics for people who want to use machine learning processes to get things done, with a strong emphasis on using existing tools and packages, rather than writing one’s own code..A companion to the author‘s .Probability and Statistics for Computer Science., this book picks up where the earlier book left off (but also supplies a summary of probability that the reader can use)..Emphasizing the usefulness ofstandard machinery from applied statistics, this textbook gives an overview of the major applied areas in learning, including coverage of:.• classification using standard machinery (naive bayes; nearest neighbor; SVM).• clustering and vector quantization (largely as in PSCS).• PCA (largely a
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Regression final example, you can think of classification as a special case of regression, where we want to predict either + 1 or − 1; this isn’t usually the best way to proceed, however. Predicting values is very useful, and so there are many examples like this.
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Learning to Classify free on the web, you would use a classifier to decide whether it was safe to run it (i.e., look at the program, and say yes or no according to some rule). As yet another example, credit card companies must decide whether a transaction is good or fraudulent.
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A Little Learning Theoryis going to behave well on test—we need some reason to be confident that this is the case. It is possible to bound test error from training error. The bounds are all far too loose to have any practical significance, but their presence is reassuring.
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High Dimensional Datances, rather than correlations, because covariances can be represented in a matrix easily. High dimensional data has some nasty properties (it’s usual to lump these under the name “the curse of dimension”). The data isn’t where you think it is, and this can be a serious nuisance, making it difficult to fit complex probability models.
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Clustering Using Probability Models a natural way of obtaining soft clustering weights (which emerge from the probability model). And it provides a framework for our first encounter with an extremely powerful and general algorithm, which you should see as a very aggressive generalization of k-means.
发表于 2025-3-23 03:08:24 | 显示全部楼层
Regression: Choosing and Managing Modelsus chapter, we saw how to find outlying points and remove them. In Sect. 11.2, I will describe methods to compute a regression that is largely unaffected by outliers. The resulting methods are powerful, but fairly intricate.
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Textbook 2019 for people who want to adopt and use the main tools of machine learning, but aren’t necessarily going to want to be machine learning researchers. Intended for students in final year undergraduate or first year graduate computer science programs in machine learning, this textbook is a machine learni
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