Non-supervised learning and intensive learning of machine learning

Non-supervised learning:

In this learning mode, the input data part is identified, the part is not identified, the learning model can be used for prediction, but the model first needs to learn the internal structure of the data in order to reasonably organize the data to make predictions. The application scenarios include classification and regression, and the algorithm includes some extensions to the commonly supervised learning algorithms, which first attempt to model the non-identified data, and then predict the identified data. such as graph theory inference algorithm (graph inference) or Laplace support vector machine (Laplacian SVM).

Intensive Learning

In this learning mode, input data as feedback to the model, unlike the monitoring model, the input data is only as a check model of the wrong way, under the reinforcement learning, the input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning).

In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised learning is a hot topic because of the large number of non-identifiable data and a small amount of identifiable data. Reinforcement learning is more used in robot control and other areas where system control is required.