list of machine learning models

generalization error;Easy to explain;Low computational complexity;Disadvantages:It is sensitive to the selection of parameters and kernel functions;The original SVM is only better at dealing with two classification problems;Boosting:Mainly take AdaBoost as an example, first look at the flow chart of AdaBoost, as follows:As you can see, we need to train several weak classifiers during training (3 in the figure), each weak classifier is trained by a sample of different weights (5 training samples

called a training sample, and we will use the data set for learning-M training sample list \ ((x^{(i)}\),\ (y^{(i) }）; i= 1,...,m-is\) is called the training set. Notice that the superscript "(i)" in the symbol is just an index in the training set, regardless of the exponentiation. We will also use X to represent the space for the input value, and Y to represent the space for the output value. In this exam

Machine learning can be divided into several types according to different computational results. These different purposes determine that machine learning can be divided into different models and classifications in practical applications.As mentioned earlier ,

. The challenge now is how to make it effective, especially for developers and the data scientists who are preparing to use it. To this end, we have collected some of the most common and useful open-source machine learning tools, through this article to share with you. Python Data scientists are embracing python in the hope that there is another more open alternative to the R language, and many employers to

This lesson mainly describes the processing of linear models. Including: 1. Input Representation) 2. Linear Classification) 3. Linear Regression) 4. nonlinear transformation) The author believes that to test the availability of a model, it is to use real data to do a good job. To explain how to apply linear models, the author uses linear models to solve the prob

Example Response message: This section shows the JSON data format for the response message of the Web service, which includes the full JSON record (curly brace representation), the data table definition (datatabble), a series of columns in the datasheet (ColumnNames), The data type (columntypes) and the returned data values (values) for each column, where the fields in the data values list are separated by commas. An example of the response

name of the person, explain the date, time and quantity and so on. It was originally developed for English, but it is now supported in Chinese. h2o--machine learning and predictive analytics framework H2O is a distributed, memory-based, extensible machine learning and predictive analytics framework for building l

: data is not labeled, but data can be grouped based on similarity and other measures of the natural structure of data. You can refer to the list of the above 10 examples: Managing photos Based on faces rather than names. In this way, the user has to name the group, such as iPhoto on Mac. Rule Extraction: data is used as the basis for extracting proposal rules (premise/result, also known as if. These rules may, but not all point to each other. This m

default is to use a hidden layer is a reasonable choice, but if you want to choose the most appropriate layer of hidden layer, you can also try to split the data into training sets, validation sets and test sets, and then try to use a hidden layer of neural network to train the model. Then try two, three hidden layers, and so on. Then see which neural network behaves best on the cross-validation set. That means you get three neural network models, on

specific research. For example, in the recommendation system, after basic computing, the competition still fails to achieve good results, because data preprocessing is very important, whether in the competition or in the project, data preprocessing requires many machine learning algorithms. I don't know much about my work, but what I learned after three months of practice is that

) = P (A, B)/P (B), which can be P (, b) = P (A | B) * P (B ). the Bayesian formula is introduced in this way. A general idea of this article: First, let's talk about a basic Bayesian learning framework that I have summarized, and then give a few simple examples to illustrate these frameworks, finally, I would like to give a more complex example, which is explained by the modules in the Bayesian machine

Novice Learning machine learning is very difficult, is to collect data is also very laborious. Fortunately, Robbie Allen collects the most comprehensive list of fast-track tables on machine learning, Python and related mathematics

I. Introduction of supervised learningThe supervised machine learning problem is nothing more than "Minimizeyour error while regularizing your parameters", which is to minimize errors while the parameters are being parameterized. The minimization error is to let our model fit our training data, and the rule parameter is to prevent our model from overfitting our training data. What a minimalist philosophy! B

Finally the end of the final, look at others summary: http://blog.sina.com.cn/s/blog_641289eb0101dynu.htmlContact Machine Learning also has a few years, but still only a rookie, when the first contact English is not good, do not understand the class, what things are smattering. After learning some open classes and books on the go, I began to understand some conce

document vector space, a fixed class collection C={c1,c2,..., CJ}, and a category called a label. Obviously, the document vector space is a high dimensional space. We have a bunch of tagged documents set For this one-sentence document, we classify it in the US, which is labeled "the". We expect to use some kind of training algorithm to train a function γ to map documents to a certain category: Γ:x→c This type of learning is called supervised

scope of this model, such as medical diagnosis and most machine learning. However, it also has some controversy. When it comes to this, it will go back to the topic of debate between the Bayesian School and the frequency School for several hundred years, because the Bayesian school assumes some prior probabilities, in contrast, the frequency school thinks that this anterior is somewhat subjective, and the

equal to the distance between the other two. This red line is the hyperplane that SVM is looking for in two-dimensional situations. It is used for binary classification data. The point supporting the other two online is the so-called support vector. We can see that there is no sample in the middle of the hyperplane and the other two lines. After finding this hyperplane, we use the mathematical representation of the hyperplane data to perform binary classification of the sample data, which is th

* *.Second, installation Scikit-learnExecute command:Conda Install Scikit-learnSecond, installation KrasExecute command:Conda Install KerasThe required tensorflow is automatically installation during installation of the Keras process.At this point, deep learning, machine learning development environment has been installed, you can commandSpyderOrJupyter Notebook