Non-supervised learning and intensive learning of machine learning

non-supervised learning:

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In this way of learning. The input data part is identified, some are not identified, such a learning model can be used to predict, but the model first need to learn the internal structure of the data in order to reasonably organize the data to be pre-measured. The application scenario includes classification and regression, and the algorithm includes some extensions to the frequently used supervised learning algorithms, which first attempt to model the non-identified data and then pre-test the identified data.

On the inference algorithm (Graph inference) or Laplace support vector machine (Laplacian SVM).

Intensive Learning

In this mode of learning. Input data as feedback to the model. Not like the oversight model. Input data However, as a way to check the error of the model, in the reinforcement learning, the input data directly to the model, the model must be adjusted immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning).

In the scenario where enterprise data is applied. The most common use of people is the model of supervised learning and non-supervised learning. In the field of image recognition. Because of the large number of non-identifiable data and a small amount of identifiable data, semi-supervised learning is a very hot topic at the moment. Reinforcement learning is a lot of other applications in robot control and other areas where system control is required.

Non-supervised learning and intensive learning of machine learning