foundations of linear and generalized linear models

Classification and logistic regression (classification and logistic regression)Http://www.cnblogs.com/czdbest/p/5768467.htmlGeneralized linear model (generalized Linear Models)Http://www.cnblogs.com/czdbest/p/5769326.htmlGenerate Learning Algorithm (generative learning algorithms)Http://www.cnblogs.com/czdbest/p/577150

Supervised Learning issues: 1. Linear regression Model: Applies to the independent variable x and the dependent variable y for The linear relationship 2, the generalized linear model: One area change in the input space affects all other areas, as follows: dividing the input space into several regions and then fitt

In the linear regression problem, we assume that in the classification problem, we assume that they are all examples of generalized linear models, and the generalized linear model is the estimation of the

This paragraph is mainly about the definition and assumption of the generalized linear model, in order to see the logical regression, we have to read the patience. 1.The Exponential family exponential distribution family Because the generalized linear model is around the exponential distribution family, it ne

ordinary Least squares Ordinary least squaresWhen the minimum value is reached, the best fit line is reachedThe minimum value of the coefficient w minimum two quadratic equation can be obtained by using the partial derivative of the WAnother form of expression that is equivalent to the above:can also be simplified intoDerivation process:Ridge Regression Ridge returnThe problem arises because the upper form is in multiple collinearity and becomes 0.This problem can be eliminated by transforming i

linear regression is based on the hypothesis of Gaussian distribution, and the Logistic regression is based on the hypothesis of Bernoulli distribution. If linear regression and Logistic regression cannot be understood from the perspective of probability, it is impossible to understand generalized linear regression by

So far, we've talked about the regression and classification examples, in the regression example:In the classification example:As you can see, μ and Φ are defined as functions of x and θ.As we'll see in this article, these two models are actually just a special case of a broad model family, generalized linear models. W

dependent variable distribution, connection function and other combinations, can get a variety of different generalized linear models.It is important to note that although generalized linear models do not require dependent variables to be normally distributed, they are requ

We begin to contact linear equations from junior middle school, and linearity is the simplest relationship between variables, so I intend to start with the linear model to introduce the basic algorithm of machine learning. Generalized linear model (General Linear MODEL,GLM)

Both logistic regression and linear regression are one of the generalized linear models, and then let's explain why this is the case.1. Exponential family distributionExponential family distribution and exponential distribution are not the same, in the probability of statistical distribution can be expressed in the exp

Outline: Review multivariate linear regression The basic form of generalized linear model Logarithmic linear regression Learning and reference 1. Review multivariate linear regressionIn the last essay, the basic form of a

As a fan of machine learning, he has recently been studying with Andrew Ng's machines learning. In the first part of the handout, Ng first explains what is called supervised learning, secondly, the linear model solved by least squares, the logistics regression of the response function by using the SIGMOD function, and then, using these two models, a widely used exponential distribution family is introduced.

Principles of multivariate linear models, python code, and Linear Models Share URL: http://www.cnblogs.com/DesertHero2013/p/7662721.html 1) Goal: Use a linear combination of attributes to make a prediction model. That is: Where is, after w and B are learned, the model is de

Reprint Please specify source: http://www.cnblogs.com/BYRans/The previous article has introduced a regression and an example of a classification. In the logistic regression model we assume that:In the classification problem we assume that:They are all examples of generalized linear models that need to understand the exponential distribution family before understa

Author: Snow Mountain Elephant Link: https://www.zhihu.com/question/27938684/answer/38730824 Source: Know Copyright belongs to the author. Commercial reprint please contact the author to obtain authorization, non-commercial reprint please indicate the source. The right to talk about their own understanding. First of all, the main problem is wrong, GLM generally refers to the generalized linear model, t

The Linear Prediction of independent variables in the classic linear model is the estimated value of the dependent variable. Generalized Linear Model: The linear prediction function of independent variables is the estimated value of the dependent variable. Common

GLM Generalized linear model George Box said: "All models is wrong, some is useful" 1. Starting with the Linear Model As a foundation of GLM, this section review the classic Linear Regression, and expounds some basic terms.The basic formula for our

doing linear regression, we are concerned about the mean and the standard deviation does not affect the model of learning and parameter θ choice, so here σ set to 1 easy to calculate)2. Three assumptions that form a generalized linear model P (y | x;θ) ∼exponentialfamily (η). The conditional probability distribution of output variable based on input var

The Linear regression model and the logistic regression model have been reviewed recently, but some of these questions are puzzling, knowing that I see a generalized linear model that is generalized Linear Model before it dawned on the original

returnWhen the classification problem is no longer two yuan but K yuan, that is, y∈{1,2,..., k}. We can solve this classification problem by constructing the generalized linear model. The following steps are described.Suppose y obeys exponential family distribution, φi = P (y = i;φ) and known. So. We also define.Also 1{} The condition for the representation in parentheses is the true value of the entire eq