Various algorithms for machine learning (2)

Continue with the original algorithm to continue the explanation:

(5) Bayesian method
Bayesian algorithm is a kind of algorithm based on Bayesian theorem, which is mainly used to solve the problem of classification and regression. Common algorithms include: naive Bayesian algorithm, average single-dependency estimation (averaged one-dependence estimators, Aode), and Bayesian belief Network (BBN).

(6) kernel-based algorithms

The most famous of kernel-based algorithms is support vector machine (SVM). The kernel-based algorithm maps the input data to a higher-order vector space, in which some classification or regression problems can be solved more easily. Common kernel-based algorithms include: Support Vector machines (SVM), Radial basis functions (Radial Basis function, RBF), and linear discriminant analysis (Linear discriminate analyses , LDA) and so on.

(7) Clustering algorithm

Clustering, like regression, is sometimes described as a kind of problem, sometimes describing a class of algorithms. Clustering algorithms typically merge input data by either a central point or a hierarchical approach. So the clustering algorithm tries to find the intrinsic structure of the data in order to classify the data in the most common way. Common clustering algorithms include the K-means algorithm and the desired maximization algorithm (expectation maximization, EM).

(8) Association Rules Learning

Association rule Learning finds useful association rules in a large number of multivariate datasets by finding rules that best explain the relationship between data variables. Common algorithms include Apriori algorithm and Eclat algorithm.

(9) Artificial neural network

Artificial Neural network algorithm is a kind of pattern matching algorithm simulating biological neural network. Typically used to solve classification and regression problems. Artificial neural network is a huge branch of machine learning, there are hundreds of kinds of different algorithms. (Deep learning is one of these algorithms, which we will discuss separately), important artificial neural network algorithms include: Perceptron Neural Networks (Perceptron neural network), reverse transfer (back propagation), Hopfield network, Self-organizing mappings (self-organizing map, SOM). Learning vector quantization (learning vector quantization, LVQ)

(10) deep learning

Deep learning algorithm is the development of artificial neural network.   In the near future won a lot of attention, especially Baidu also began to exert deep learning, is in the domestic caused a lot of concern. In today's increasingly inexpensive computing power, deep learning attempts to build a much larger and more complex neural network. Many deep learning algorithms are semi-supervised learning algorithms used to handle large datasets with small amounts of data that are not identified. Common depth learning algorithms include: Restricted Boltzmann machines (Restricted Boltzmann machine, RBN), deep belief Networks (DBN), convolutional networks (convolutional network), Stack-type Automatic encoder (stacked auto-encoders).