Machine learning is the core of artificial intelligence (AI) and the fundamental way to make computers have intelligence.
This paper sorts out 15 terms commonly used in the field of machine learning, hoping to help you better understand this complex subject involving probability theory, statistics, approximation theory, convex analysis, algorithm complexity theory and other fields.
1. AdaBoost(Adaptive Boosting)
AdaBoost is short for adaptive boosting.
AdaBoost is an iterative algorithm. Its core idea is to train different classifiers (weak classifiers) for the same training set, and then combine these weak classifiers to form a stronger final classifier (strong classifier).
2. Random forest
Random forest belongs to the method of bagging (short for bootstrap aggregation) in integrated learning.
In machine learning, random forest is a classifier with multiple decision trees, and the output category is determined by the mode of the output category of individual tree.
Four steps of constructing random forest
3. Unsupervised learning
Unsupervised learning is a branch of machine learning, which is learned from unlabeled or classified test data. It is essentially a statistical tool that can find potential structures in unlabeled data.
Unsupervised learning is not to respond to feedback, but to identify and respond to the commonness in each new data according to whether it exists.
4. Supervised learning
Supervised learning is a process that uses a set of samples of known categories to adjust the parameters of the classifier to achieve the required performance.
Two tasks of supervised learning
Supervised learning is to infer a functional machine learning task from labeled training data. In supervised learning, each instance is composed of an input object (usually a vector) and an expected output value (also known as a supervised signal). Supervised learning algorithm is a function of analyzing the training data and generating an inference, which can be used to map out new instances.
5. Deep learning
Deep learning is a kind of representation learning method based on data in machine learning.
The relationship between deep learning, artificial intelligence and machine learning
It belongs to the category of machine learning. It can be said that it is an upgrade based on the traditional neural network, which is about equal to the neural network. Its advantage is to use unsupervised or semi supervised feature learning and hierarchical feature extraction algorithm instead of manual feature acquisition.
Deep learning is a new field in machine learning. Its motivation is to build and simulate neural network of human brain for analysis and learning. It imitates the mechanism of human brain to interpret data, such as image, voice and text.
6. Reinforcement learning
Reinforcement learning, also known as reinforcement learning and evaluation learning, is an important machine learning method, which has many applications in intelligent control robot, analysis and prediction and other fields.
In reinforcement learning, machines will get positive reinforcement when they achieve the expected results, and negative reinforcement when they do not achieve the expected results.
7. K-means clustering
K-means clustering algorithm firstly randomly selects K objects as the initial clustering center, then calculates the distance between each object and each seed clustering center, and assigns each object to the nearest clustering center.
Cluster centers and objects assigned to them represent a cluster. Once all the objects are assigned, the cluster center of each cluster will be recalculated according to the existing objects in the cluster.
This process will be repeated until a termination condition is met. The termination condition can be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers change again, and the sum of error squares is locally minimum.
8. Cluster analysis
Cluster analysis refers to the analysis process of grouping a set of physical or abstract objects into multiple classes composed of similar objects.
The goal of cluster analysis is to collect data to classify on the basis of similarity. Clustering comes from many fields, including mathematics, computer science, statistics, biology and economics.
9. Ensemble learning
Integrated learning is a machine learning method which uses a series of learners and uses some rules to integrate the learning results so as to obtain better learning effect than a single learner.
10. Support vector machine
In machine learning, support vector machine (SVM, also support vector network) is a supervised learning model related to related learning algorithms, which can analyze data, identify patterns, and be used for classification and regression analysis.
Support vector machine is one of the most popular and concerned machine learning algorithms.
11. Decision tree
Decision tree algorithm is a method to approach the value of discrete function. It is one of the predictive modeling methods used in statistics, data mining and machine learning.
12. Logistic regression
Logical regression is a kind of generalized linear regression analysis model, which is often used in data mining, automatic disease diagnosis, economic prediction and other fields. Logical regression mainly solves the problem of classification, which is used to express the possibility of something happening.
13. Naive Bayes classifier
Naive Bayes is a simple but surprisingly powerful predictive modeling algorithm.
Naive Bayes is called naive because it assumes that each input variable is independent. This is a strong assumption, which is not realistic for real data. However, this technology is very effective for large-scale complex problems.
14. Linear regression
Linear regression, originally a statistical concept, is now often used in machine learning.
If there is a “linear relationship” between two or more variables, we can find out the “routine” between variables through historical data, and build an effective model to predict the future variable results.
15. Machine learning
Machine learning studies how computers simulate or implement human learning behavior to acquire new knowledge or skills, and reorganize the existing knowledge structure to improve its performance.
It is the core of artificial intelligence and the fundamental way to make computers have intelligence. Its application covers all fields of artificial intelligence. It mainly uses induction, synthesis rather than deduction.