Machine Learning Algorithm Counting

Machine learning is undoubtedly a hot topic in the field of current data analysis. Many people use machine learning algorithms more or less in their usual work. Here we will summarize the common machine learning algorithms for you to reference in your work and learning.

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

Learning Style

Depending on the type of data, there are different ways to model a problem. In the field of machine learning or artificial intelligence, people will first consider the algorithm's learning style. In the field of machine learning, there are several main ways of learning. It is a good idea to classify the algorithm according to the way of learning, which allows people to consider the best possible results by choosing the most suitable algorithm based on the input data when modeling and algorithm selection.

　　Supervised Learning:

Under supervised learning, the input data is called "training data", each set of training data has a clear identification or results, such as the anti-spam system "spam" "non-spam", the handwritten numeral recognition of "1", "2", "3", "4" and so on. In the establishment of the predictive model, supervised learning establishes a learning process, compares the predicted results with the actual results of the "training data", and adjusts the predictive model continuously until the predicted results of the model reach an expected accuracy rate. Common application scenarios for supervised learning such as classification problems and regression problems. Common algorithms include logistic Regression and reverse transfer neural networks (back propagation neural network)

　　 non-supervised learning:

In unsupervised learning, the data is not specifically identified, and the learning model is designed to infer some intrinsic structure of the data. Common application scenarios include learning about 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 part is identified, the part is not identified, the learning model can be used for prediction, but the model first needs to learn the internal structure of the data in order to reasonably organize the data to make predictions. The application scenarios include classification and regression, and the algorithm includes some extensions to the commonly supervised learning algorithms, which first attempt to model the non-identified data, and then predict the identified data. On the inference algorithm (Graph inference) or Laplace support vector machine (Laplacian SVM).

　　 Intensive Learning:in this learning mode, input data as feedback to the model, unlike the monitoring model, the input data is only as a check model of the wrong way, under the reinforcement learning, the input data directly feedback to the model, the model must be immediately adjusted. Common application scenarios include dynamic systems and robot control. Common algorithms include q-learning and time difference learning (temporal difference learning)

In the case of enterprise Data application, the most commonly used is the model of supervised learning and unsupervised learning. In the field of image recognition, semi-supervised learning is a hot topic because of the large number of non-identifiable data and a small amount of identifiable data. Reinforcement learning is more used in robot control and other areas where system control is required.

　　Algorithmic similarity

According to the function and form similarity of the algorithm, we can classify the algorithm, for example, 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 classify into a certain category. For some classifications, the same classification algorithm can be used for different types of problems. Here, we try to classify commonly used algorithms in the easiest way to understand them.

　　 Regression Algorithm

The regression algorithm is a kind of algorithm that tries to use the measurement of error to explore the relationship between variables. Regression algorithm is a powerful tool for statistical machine learning. In the field of machine learning, people talk about regression, sometimes refers to a kind of problem, sometimes refers to a kind of algorithm, which often makes beginners confused. Common regression algorithms include: least squares (ordinary Least square), Logistic regression (logistic Regression), stepwise regression (stepwise Regression), multiple adaptive regression splines (multivariate Adaptive Regression splines) and local scatter smoothing estimation (locally estimated scatterplot smoothing)

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Machine Learning Algorithm Counting