What is supervised learning and unsupervised learning?

Difference between supervised and unsupervised learning:

1. Different principles

Supervised learning refers to the process of adjusting the parameters of a classifier using a set of samples of a known category to achieve the required performance. Unsupervised learning refers to the process of solving various problems in pattern recognition based on training samples of unknown (unlabeled) categories.

2, different algorithms

Supervised learning algorithms are generated by analyzing training data of known categories. Unsupervised learning algorithms are mainly principal component analysis methods, isometric mapping methods, local linear embedding methods, Laplace feature mapping methods, Hessian local linear embedding methods, and local tangent space arrangement methods.

3, different conditions

Supervised learning applies to the sample data is known. Unsupervised learning applies to the case of no category information.

Reference: Baidu Encyclopedia - Supervised Learning, Baidu Encyclopedia - Unsupervised Learning