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

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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.

前兆

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.

吹牛需要藝術(shù)

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.

預(yù)兆好

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.

解決

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.

miracle

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.

幼稚

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