Three cornerstones of artificial intelligence— algorithm, Data and computing power, Algorithm as one of the others, It's very important, So what algorithms will artificial intelligence involve?? Which scenarios are different algorithms applicable to??

One, According to the different training methods of the model, it can be divided into supervised learning.(Supervised Learning), Unsupervised learning(Unsupervised
Learning), Semi supervised learning(Semi-supervised Learning) And strengthening learning(Reinforcement Learning) Four broad categories.

Common supervised learning algorithms include the following categories:
(1) artificial neural network(Artificial Neural Network) class: Back propagation(Backpropagation), Boltzmann machine(Boltzmann
Machine), Convolutional neural network(Convolutional Neural Network),Hopfield network(hopfield
Network), Multilayer perceptron(Multilyer Perceptron), Radial basis function network(Radial Basis Function
Network,RBFN), Restricted Boltzmann Machines (Restricted Boltzmann Machine), Regression neural network(Recurrent Neural
Network,RNN), Self organizing mapping(Self-organizing Map,SOM), Spike neural network(Spiking Neural Network) etc..
(2) Bayes class(Bayesin): Naive Bayes (Naive Bayes), Gaussian Bayes(Gaussian Naive
Bayes), Multinomial naive Bayes(Multinomial Naive Bayes), Average- Dependency assessment(Averaged One-Dependence
Estimators,AODE)
Bayesian belief network(Bayesian Belief Network,BBN), bayesian network (Bayesian Network,BN) etc..
(3) Decision tree(Decision Tree) class: Classification and regression tree(Classification and Regression
Tree,CART), iterationDichotomiser3(Iterative Dichotomiser 3, ID3),C4.5 algorithm(C4.5
Algorithm),C5.0 algorithm(C5.0 Algorithm), Card side automatic interactive detection(Chi-squared Automatic Interaction
Detection,CHAID), Decision residue(Decision Stump),ID3 algorithm(ID3 Algorithm), Random forest(Random
Forest),SLIQ(Supervised Learning in Quest) etc..
(4) Linear classifier(Linear Classifier) class:Fisher Linear discrimination of(Fisher’s Linear Discriminant)
linear regression(Linear Regression), logistic regression(Logistic Regression), Multiple logistic regression(Multionmial Logistic
Regression), Naive Bayes classifier (Naive Bayes Classifier), perception(Perception), Support vector machine(Support
Vector Machine) etc..

Common unsupervised learning algorithms include:
(1) artificial neural network(Artificial Neural Network) class: Generate countermeasure network(Generative Adversarial
Networks,GAN), Feedforward neural network(Feedforward Neural Network), Logic learning machine(Logic Learning
Machine), Self organizing mapping(Self-organizing Map) etc..
(2) Association rule learning(Association Rule Learning) class: Prior algorithm(Apriori Algorithm),Eclat algorithm(Eclat
Algorithm),FP-Growth Algorithm, etc..
(3) Hierarchical clustering algorithm(Hierarchical Clustering): Single linkage clustering(Single-linkage
Clustering), Concept clustering(Conceptual Clustering) etc..
(4) cluster analysis(Cluster
analysis):BIRCH algorithm,DBSCAN algorithm, Expectation maximization(Expectation-maximization,EM), Fuzzy clustering(Fuzzy
Clustering),K-means algorithm,K Mean clustering(K-means
Clustering),K-medians clustering, Mean shift algorithm(Mean-shift),OPTICS Algorithm, etc..
(5) anomaly detection(Anomaly detection) class:K Nearest neighbor(K-nearest Neighbor,KNN) algorithm, Local anomaly factor algorithm(Local
Outlier Factor,LOF) etc..

Common semi supervised learning algorithms include: Generating model(Generative Models), Low density separation(Low-density
Separation), Graphic based approach(Graph-based Methods), Joint training(Co-training) etc..


Common reinforcement learning algorithms include:Q Study(Q-learning), state- Get some action- reward- state- Get some action(State-Action-Reward-State-Action,SARSA),DQN(Deep
Q Network), Strategy gradient algorithm(Policy Gradients), Model based reinforcement learning(Model Based RL), Sequential differential learning(Temporal
Different Learning) etc..

Common deep learning algorithms include: Deep belief network(Deep Belief Machines), Deep convolution neural network(Deep Convolutional Neural
Networks), Deep recurrent neural network(Deep Recurrent Neural Network), Hierarchical time memory(Hierarchical Temporal
Memory,HTM), Deep Boltzmann machine(Deep Boltzmann Machine,DBM), Trestle type automatic encoder(Stacked
Autoencoder), Generate countermeasure network(Generative Adversarial Networks) etc..

Two, Classification according to different tasks to be solved, It can be roughly divided into two classification algorithms(Two-class Classification), Multi classification algorithm(Multi-class
Classification), Regression algorithm(Regression), clustering algorithm(Clustering) And anomaly detection(Anomaly Detection) Five kinds.
1. Two classification(Two-class Classification)
(1) Binary support vector machine(Two-class SVM): More data features, Scene of linear model.
(2) Dichotomous average perceptron(Two-class Average Perceptron): Short training time, Scene of linear model.
(3) Binary logistic regression(Two-class Logistic Regression): Short training time, Scene of linear model.
(4) Binary Bayes point machine(Two-class Bayes Point
Machine): Short training time, Scene of linear model.(5) Dichotomous decision forest(Two-class Decision Forest): Short training time, Precise scene.
(6) Two classification promotion decision tree(Two-class Boosted Decision Tree): Short training time, High accuracy, Scenarios with large memory consumption
(7) Binary decision jungle(Two-class Decision Jungle): Short training time, High accuracy, Scenarios with small memory consumption.
(8) Binary classification local depth support vector machine(Two-class Locally Deep SVM): Suitable for scenes with more data features.
(9) Binary neural network(Two-class Neural Network): Suitable for high accuracy, Scenes with long training time.


Three solutions are usually applied to solve multi classification problems: First kind, Starting with data sets and applicable methods, Using two classifiers to solve the problem of multi classification; Second kinds, Direct use of multiple classifiers with multiple classification capabilities; Third kinds, Improving two classifiers to multi classifiers to solve the problem of multi classification.
Common algorithms:
(1) Multiclass logistic regression(Multiclass Logistic Regression): Short training time, Scene of linear model.
(2) Multiclassification neural network(Multiclass Neural Network): Suitable for high accuracy, Scenes with long training time.
(3) Multi category decision forest(Multiclass Decision Forest): Suitable for high accuracy, Scenes with short training time.
(4) Multi category decision jungle(Multiclass Decision Jungle): Suitable for high accuracy, Scenarios with small memory consumption.
(5)“ One to many” Multi class(One-vs-all Multiclass): Depends on the effect of two classifiers.

regression

Regression problems are often used to predict specific values rather than to classify them. Except for the returned results, Other methods are similar to classification problems. We will output quantitatively, Or continuous variable prediction is called regression.; Qualitative output, Or discrete variable prediction is called classification.. The algorithm of long towel is:
(1) Sorted regression(Ordinal Regression): Scenario applicable to sorting data.
(2) Poisson regression(Poission Regression): Scenarios for predicting the number of events.
(3) Fast forest quantile regression(Fast Forest Quantile Regression): Scenarios suitable for predicted distribution.
(4) linear regression(Linear Regression): Short training time, Scene of linear model.
(5) Bayesian linear regression(Bayesian Linear Regression): For linear models, Scenarios with less training data.
(6) Neural network regression(Neural Network Regression): Suitable for high accuracy, Scenes with long training time.
(7) Decision making forest return(Decision Forest Regression): Suitable for high accuracy, Scenes with short training time.
(8) Promote decision tree regression(Boosted Decision Tree Regression): Suitable for high accuracy, Short training time, Memory intensive scenarios.

clustering
The goal of clustering is to discover the potential laws and structures of data. Clustering is often used to describe and measure the similarity between different data sources, And classify the data sources into different clusters..
(1) hierarchical clustering(Hierarchical Clustering): Short training time, Large data scenarios.
(2)K-means algorithm: Suitable for high accuracy, Scenes with short training time.
(3) Fuzzy clusteringFCM algorithm(Fuzzy C-means,FCM): Suitable for high accuracy, Scenes with short training time.
(4)SOM neural network(Self-organizing Feature Map,SOM): Suitable for long-running scenarios.
anomaly detection
Abnormal detection refers to the detection and marking of abnormal or atypical segments in data. Sometimes referred to as deviation detection.

Anomaly detection looks very similar to supervised learning problems, It's all about classification.. It is to predict and judge the labels of samples. But in fact, the difference between the two is very big. Because of positive samples in anomaly detection( Outliers) Very small. Common algorithms are:
(1) A classification support vector machine(One-class SVM): Suitable for scenes with more data features.
(2) Be based onPCA Anomaly detection of(PCA-based Anomaly Detection): Suitable for short training time.

Common migration learning algorithms include: Inductive transfer learning(Inductive Transfer Learning) , Direct transfer learning(Transductive
Transfer Learning), Unsupervised transfer learning(Unsupervised Transfer Learning), Transfer learning(Transitive
Transfer Learning) etc..

Applicable scenarios of the algorithm:
Factors to consider include:
(1) Size of data volume, Data quality and characteristics of data itself
(2) What is the nature of the problem in the specific business scenario to be solved by machine learning?
(3) What is the acceptable calculation time?
(4) How high is the algorithm accuracy required?




Algorithm is available. With the trained data( Preprocessed data), So many times( It's time to test computing power) after, After model evaluation and algorithm personnel parameter adjustment, You will get the training model. When new data is entered, Then our training model will give the result.. The most basic functions required by the business are realized..

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Reference resources:《 AI Product Manager–AI timesPM Training Manual》
author: Zhang Jing Yu