Unsupervised Learning

最新推荐文章于 2024-04-14 16:17:29 发布

转载最新推荐文章于 2024-04-14 16:17:29 发布 · 120 阅读

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原文链接：https://www.coursera.org/learn/machine-learning

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本文介绍了无监督学习的基本概念及其应用场景，重点讲述了如何通过数据内在的结构和变量间的关系进行聚类分析，同时探讨了非聚类场景下从混沌环境中发现规律的方法。

Unsupervised Learning

Unsupervised learning allows us to approach problems with little or no idea what our results should look like. We can derive structure from data where we don’t necessarily know the effect of the variables.

We can derive this structure by clustering the data based on relationships among the variables in the data.

With unsupervised learning there is no feedback based on the prediction results.

Example:

Clustering: Take a collection of 1,000,000 different genes, and find a way to automatically group these genes into groups that are somehow similar or related by different variables, such as lifespan, location, roles, and so on.

Non-clustering: The “Cocktail Party Algorithm”, allows you to find structure in a chaotic environment. (i.e. identifying individual voices and music from a mesh of sounds at a cocktail party).