Common Machine Learning Algorithms Collected by Artificial Intelligence Engineers

Common Machine Learning Algorithms Collected by Artificial Intelligence Engineers

Common Machine Learning Algorithms Collected by Artificial Intelligence Engineers



Machine learning


Machine learning is the most important part of artificial intelligence. Artificial intelligence is different from ordinary big data analysis in that it has the characteristics of machine learning, which is equivalent to having a brain. The following are commonly used machine learning algorithms.

There are many algorithms for machine learning. Many times people are confused. Many algorithms are a type of algorithm, and some algorithms are extended from other algorithms. Here, we introduce you from two aspects, the first aspect is the way of learning, and the second aspect is the similarity of the algorithm.


Learning Method


There are different ways to model a problem, depending on the type of data. In the field of machine learning or artificial intelligence, people first consider the way the algorithm learns. There are several main ways of learning in machine learning. It is a good idea to classify the algorithm according to the learning method. This allows people to consider the most appropriate algorithm based on the input data to get the best results when modeling and algorithm selection.


Reinforcement learning

In this learning mode, the input data is used as feedback to the model. Unlike the supervised model, the input data is only used as a way to check the model right or wrong. Under reinforcement learning, the input data is directly fed back to the model. The model must Make adjustments immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-Learning and Temporal difference learning.

In the context of enterprise data applications, the most commonly used models are the models of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised learning is a hot topic due to the large amount of non-identified data and a small amount of identifiable data. More applications for reinforcement learning are in robot control and other areas where system control is required.


Supervised learning

Under supervised learning, the input data is called “training data”, and each set of training data has a clear identification or result, such as “spam” and “non-spam” in the anti-spam system, in handwritten digit recognition. "1", "2", "3", "4" and so on. When establishing a predictive model, supervised learning establishes a learning process, compares the predicted results with the actual results of the “training data”, and continuously adjusts the predictive model until the predicted results of the model reach an expected accuracy. Common application scenarios for supervised learning such as classification and regression. Common algorithms include Logistic Regression and Back Propagation Neural Network.

Unsupervised learning:

In unsupervised learning, data is not specifically identified, and the learning model is used to infer some of the inherent structure of the data. Common application scenarios include learning of association rules and clustering. Common algorithms include the Apriori algorithm and the k-Means algorithm.

Semi-supervised learning:

In this learning mode, the input data portion is identified and the portion is not identified. This learning model can be used to make predictions, but the model first needs to learn the internal structure of the data in order to reasonably organize the data for prediction. The application scenario includes classification and regression. The algorithm includes some extensions to the commonly used supervised learning algorithms. These algorithms first attempt to model the unidentified data, and then predict the identified data. Graph Inference or Laplacian SVM.

Algorithm similarity


According to the similarity of the function and form of the algorithm, we can classify the algorithm, such as tree-based algorithm, neural network-based algorithm and so on. Of course, the scope of machine learning is very large, and some algorithms are difficult to clearly classify into a certain class. For some classifications, the same classification algorithm can target different types of problems. Here, we try to classify commonly used algorithms in the most understandable way.

Regression algorithm:

Regression algorithms are a type of algorithm that attempts to explore the relationship between variables using a measure of error. The regression algorithm is a tool for statistical machine learning. In the field of machine learning, people talk about regression, sometimes referring to a type of problem, sometimes referring to a type of algorithm, which often confuses beginners. Common regression algorithms include: Ordinary Least Square, Logistic Regression, Stepwise Regression, Multivariate Adaptive Regression Splines, and Local Scattering Smooth Estimation (Locally Estimated Scatterplot Smoothing)

Instance-based algorithm


Instance-based algorithms are often used to model decision problems. Such models often select a batch of sample data and then compare the new data to the sample data based on some approximation. In this way, find the best match. Therefore, instance-based algorithms are often referred to as "winner-take-all" learning or "memory-based learning." Common algorithms include k-Nearest Neighbor (KNN), Learning Vector Quantization (LVQ), and Self-Organizing Map (SOM).

Regularization method


The regularization method is an extension of other algorithms (usually regression algorithms) that adjust the algorithm according to the complexity of the algorithm. Regularization methods typically reward simple models and penalize complex algorithms. Common algorithms include: Ridge Regression, Least Absolute Shrinkage and Selection Operator (LASSO), and Elastic Net.

Decision tree learning

The decision tree algorithm uses a tree structure to establish a decision model based on the attributes of the data. Decision tree models are often used to solve classification and regression problems. Common algorithms include: Classification and Regression Tree (CART), ID3 (Iterative Dichotomiser 3), C4.5, Chi-squared Automatic Interaction Detection (CHAID), Decision Stump, Random Forest (Random Forest), Multiple Adaptive Regression Spline (MARS) and Gradient Boosting Machine (GBM)

Bayesian method

Bayesian method is a kind of algorithm based on Bayes' theorem, which is mainly used to solve classification and regression problems. Common algorithms include: Naive Bayes algorithm, Averaged One-Dependence Estimators (AODE), and Bayesian Belief Network (BBN).

Kernel-based algorithm

The most famous of the kernel-based algorithms is the support vector machine (SVM). Kernel-based algorithms map input data to a high-order vector space in which some classification or regression problems can be solved more easily. Common kernel-based algorithms include: Support Vector Machine (SVM), Radial Basis Function (RBF), and Linear Discriminate Analysis (LDA).

Clustering Algorithm

Clustering, like regression, sometimes describes a type of problem, sometimes describing a type of algorithm. Clustering algorithms typically merge input data in a central or hierarchical manner. So the clustering algorithm tries to find the intrinsic structure of the data in order to classify the data according to the greatest commonality. Common clustering algorithms include the k-Means algorithm and Expectation Maximization (EM).

Association rule learning

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

Artificial neural networks


Artificial neural network algorithm simulates biological neural network and is a kind of pattern matching algorithm. Usually used to solve classification and regression problems. Artificial neural networks are a huge branch of machine learning with hundreds of different algorithms. (Deep learning is one of the kinds of algorithms, we will discuss it separately.) Important artificial neural network algorithms include: Perceptron Neural Network, Back Propagation, Hopfield Network, Self-Organizing Mapping ( Self-Organizing Map, SOM). Learning Vector Quantization (LVQ)

Deep learning


Deep learning algorithms are the development of artificial neural networks. In the near future, I have won a lot of attention, especially after Baidu has begun to develop deep learning, even in China.

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