Abstract
Unsupervised learning is a kind of machine learning algorithm that can be
used to draw useful conclusions without the presence of labeled responses in the input
data. In the previous chapter, we discussed clustering (k-means clustering, hierarchical
clustering) and Principal Component Analysis. In this chapter, we will discuss training
versus testing, bounding the testing error, and the VC dimension.
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