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A common algorithm for machine learning in artificial intelligence
A common algorithm for machine learning in artificial intelligence
abstract
I have been very interested in machine learning and haven't had time to study it all the time. It's just the weekend. I have time to go to all the big technical forums. I just see a good article about the machine learning, and I'll share it here.
Machine learning is undoubtedly a hot topic in the field of data analysis. Many people use machine learning algorithms more or less in their daily work. Here, IT manager net summarizes the common machine learning algorithms for you to refer to in your work and study.
Here's a picture description
There are many algorithms for machine learning. Many times people are confused, many algorithms are a class of algorithms, and some algorithms are extended from other algorithms. Here, let's introduce from two aspects, the first aspect is the way of learning, and the second aspect is the similarity of algorithm.
Here's a picture description
learning style
Depending on the type of data, there are different ways of modeling a problem. In the field of machine learning or artificial intelligence, people first consider the way of learning algorithms. In the field of machine learning, there are several main ways of learning. It is a good idea to classify algorithms in the way of learning, which allows people to consider the best choice of algorithms to obtain the best results from the input data when the modeling and algorithms are selected.
Supervised learning:
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In supervised learning, the input data is called "training data". Each training data has a clear identification or result, such as "spam" in the anti spam system, "non spam", "1", "2", "3", "4" for handwritten digital recognition. When the prediction model is established, a learning process is established by supervised learning. The prediction results are compared with the actual results of the "training data", and the prediction model is constantly adjusted, until the prediction results of the model reach a expected accuracy. The common application scenarios of supervised learning are categorization and regression. Common algorithms include logistic regression (Logistic Regression) and Back Propagation Neural Network.
Unsupervised learning:
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In unsupervised learning, data are not specifically labeled, and the learning model is to infer some inherent structures of data. Common application scenarios include learning association rules and clustering. Common algorithms include Apriori algorithm and k-Means algorithm.
Semi supervised learning:
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In this way, the input data part is identified and the part is not identified. This learning model can be used for prediction, but the model needs to learn the internal structure of the data in order to organize data to predict. The application scenarios include classification and regression, which include the extension of some commonly used supervised learning algorithms, which first attempt to model the unidentified data and then predict the identifier data on this basis. Such as graph reasoning algorithm (Graph Inference) or Laplasse support vector machine (Laplacian SVM.).
Intensive learning:
Here's a picture description
In this learning model, input data is used as a feedback to the model. Unlike the supervised model, the input data is only a way to check the error of the model. Under intensive learning, the input data is directly fed back to the model, and the model must be adjusted immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-Learning and Temporal difference learning.
In the scenario of enterprise data application, the most commonly used model is supervised learning and unsupervised learning. In the field of image recognition, semi supervised learning is a hot topic because of a large number of unlabeled data and a small number of identifiable data. Reinforcement learning is more widely used in robot control and other areas that require systematic control.
Algorithm similarity
According to the similarity of 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 hard to categorize to a certain category. For some classifications, the same classification algorithm can be applied to different types of problems. Here, we try to classify commonly used algorithms according to the easiest way to understand them.
Regression algorithm:
Here's a picture description
Regression algorithm is trying to use the measure of error to explore the relationship between variables. Regression algorithm is a useful tool for statistical machine learning. In machine learning, people talk about regression, sometimes referring to a class of problems, sometimes referring to a class of algorithms, which often confuse beginners. The common regression algorithms include: Ordinary Least Square, logical regression (Logistic Regression), stepwise regression (Stepwise Regression), multiple adaptive regression spline (Multivariate Adaptive Regression Splines) and local scatter smoothing estimation. Hing)
Case based algorithm
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Example based algorithms are often used to model a decision problem. This model often selects a batch of sample data first, and then compares the new data with the sample data based on some approximation. This is the way to find the best match. Therefore, instance based algorithms are often referred to as "winner take all."
I have been very interested in machine learning and haven't had time to study it all the time. It's just the weekend. I have time to go to all the big technical forums. I just see a good article about the machine learning, and I'll share it here.
Machine learning is undoubtedly a hot topic in the field of data analysis. Many people use machine learning algorithms more or less in their daily work. Here, IT manager net summarizes the common machine learning algorithms for you to refer to in your work and study.
Here's a picture description
There are many algorithms for machine learning. Many times people are confused, many algorithms are a class of algorithms, and some algorithms are extended from other algorithms. Here, let's introduce from two aspects, the first aspect is the way of learning, and the second aspect is the similarity of algorithm.
Here's a picture description
learning style
Depending on the type of data, there are different ways of modeling a problem. In the field of machine learning or artificial intelligence, people first consider the way of learning algorithms. In the field of machine learning, there are several main ways of learning. It is a good idea to classify algorithms in the way of learning, which allows people to consider the best choice of algorithms to obtain the best results from the input data when the modeling and algorithms are selected.
Supervised learning:
Here's a picture description
In supervised learning, the input data is called "training data". Each training data has a clear identification or result, such as "spam" in the anti spam system, "non spam", "1", "2", "3", "4" for handwritten digital recognition. When the prediction model is established, a learning process is established by supervised learning. The prediction results are compared with the actual results of the "training data", and the prediction model is constantly adjusted, until the prediction results of the model reach a expected accuracy. The common application scenarios of supervised learning are categorization and regression. Common algorithms include logistic regression (Logistic Regression) and Back Propagation Neural Network.
Unsupervised learning:
Here's a picture description
In unsupervised learning, data are not specifically labeled, and the learning model is to infer some inherent structures of data. Common application scenarios include learning association rules and clustering. Common algorithms include Apriori algorithm and k-Means algorithm.
Semi supervised learning:
Here's a picture description
In this way, the input data part is identified and the part is not identified. This learning model can be used for prediction, but the model needs to learn the internal structure of the data in order to organize data to predict. The application scenarios include classification and regression, which include the extension of some commonly used supervised learning algorithms, which first attempt to model the unidentified data and then predict the identifier data on this basis. Such as graph reasoning algorithm (Graph Inference) or Laplasse support vector machine (Laplacian SVM.).
Intensive learning:
Here's a picture description
In this learning model, input data is used as a feedback to the model. Unlike the supervised model, the input data is only a way to check the error of the model. Under intensive learning, the input data is directly fed back to the model, and the model must be adjusted immediately. Common application scenarios include dynamic systems and robot control. Common algorithms include Q-Learning and Temporal difference learning.
In the scenario of enterprise data application, the most commonly used model is supervised learning and unsupervised learning. In the field of image recognition, semi supervised learning is a hot topic because of a large number of unlabeled data and a small number of identifiable data. Reinforcement learning is more widely used in robot control and other areas that require systematic control.
Algorithm similarity
According to the similarity of 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 hard to categorize to a certain category. For some classifications, the same classification algorithm can be applied to different types of problems. Here, we try to classify commonly used algorithms according to the easiest way to understand them.
Regression algorithm:
Here's a picture description
Regression algorithm is trying to use the measure of error to explore the relationship between variables. Regression algorithm is a useful tool for statistical machine learning. In machine learning, people talk about regression, sometimes referring to a class of problems, sometimes referring to a class of algorithms, which often confuse beginners. The common regression algorithms include: Ordinary Least Square, logical regression (Logistic Regression), stepwise regression (Stepwise Regression), multiple adaptive regression spline (Multivariate Adaptive Regression Splines) and local scatter smoothing estimation. Hing)
Case based algorithm
Here's a picture description
Example based algorithms are often used to model a decision problem. This model often selects a batch of sample data first, and then compares the new data with the sample data based on some approximation. This is the way to find the best match. Therefore, instance based algorithms are often referred to as "winner take all."