input values are all added together to get the predicted values.1, definition of regressionThe simplest definition of regression is to give a point set D, to fit the point set with a function, and to minimize the error between the point set and the fitted function, if the function curve is a straight line, it is called linear regression, and if the curve is a tw

values and all added together to obtain a pre-measured value.1, definition of regressionThe simplest definition of regression is to give a point set, D, to fit the point set with a function. Moreover, the error between the point set and the fitted function is minimized, assuming that the function curve is a straight line, it is called linear regression, and the

This article introduces the concepts of fitting and under-fitting, and introduces local weighted regression algorithms.Over fitting and under fittingBefore in linear regression, we always put the individual x as our characteristic, but in fact we can consider that even the higher times of x as our characteristics, then we will get through

1. PrefaceThe linear regression form is simple and easy to model, but it contains some important basic ideas in machine learning. Many of the more powerful non-linear models (nonlinear model) can be obtained by introducing hierarchies or high-dimensional mappings on the basis of linear models. In addition, because the

Conditions/Prerequisites for regression problems:1) The data collected2) The hypothetical model, a function, which contains unknown parameters, can be estimated by learning the parameters. The model is then used to predict/classify new data.1. Linear regressionAssume that both features and results are linear. That is, no more than one-time party. This is for the

for linear regression, logistic regression, and general regression"Turn from": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct.1 SummaryThis report is a summary and under

Understanding of linear regression, logistic regression and general regression"Please specify the source when reproduced": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct

Regression is to try to find out the number of variables in the relationship between the change in the expression of the function expression, this expression called the regression equation.
Conditions/Prerequisites for regression issues:
1) collected data
2 The hypothetical model
The model is a function that contains unknown parameters and can be estimated by lea

Original: http://www.cnblogs.com/jerrylead/archive/2011/03/05/1971867.html#3281650Understanding of linear regression, logistic regression and general regression"Please specify the source when reproduced": Http://www.cnblogs.com/jerryleadJerryleadFebruary 27, 2011As a machine learning beginner, the understanding is limi

1 multivariate linear regression model 1 multivariate regression model and regression equation
Multivariate regression model:y=β0 +β1 x 1 +β2 x 2 +...+βk x k +εMultivariate regression equation:Multiple

As a machine learning beginner, the understanding is limited, the expression also has many mistakes, hope that everybody criticizes correct.
1 Summary
This report is a summary and understanding of the first four sections of the Stanford University Machine learning program plus the accompanying handouts. The first four sections mainly describe the regression problem, and regression is a method of supervised

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

Chapter Two univariate linear regression (Linear Regression with one Variable) 1.Model RepresentationIf we return to the problem of training set (Training set) as shown in the following table:The tag we will use to describe this regression problem is as follows :M represent

Although some of the content is still not understood, first intercepted excerpts.1. Variable selection problem: from normal linear regression to lassoNormal linear regression using least squares fitting is the basic method of data modeling. The key point of the modeling is that the error term generally requires an inde

First we look at a linear regression problem, in the following example, we select the characteristics of different dimensions to fit our data.For the above three images do the following explanation:Select a feature to fit the data, it can be seen that the fitting situation is not very good, some data error is still relatively largeFor the first one, we added extra features, and we can see that the situation

called classification problem.Linear regressionSuppose the price is not only related to the area, but also to the number of bedrooms, as follows:At this time \ (x\) is a 2-dimensional vector \ (\in \mathbb{r^2}\). where \ (x_1^{(i)}\) represents the house area of the first ( i\) sample,\ (x_2^{(i)}\) represents the number of house bedrooms for the first \ (i\) sample.We now decide to approximate y as the linear function of x, which is the following f

-----------------------------Author:midu---------------------------qq:1327706646------------------------datetime:2014-12-08 02:29(1) PrefaceBefore looking at the least squares, has been very vague, the back yesterday saw the MIT linear algebra matrix projection and the least squares, suddenly a sense of enlightened, the teacher put him from the angle of the equation and the matrix, and have a different understanding. In fact, it is very simple to find

Original: http://blog.csdn.net/abcjennifer/article/details/7700772This column (machine learning) includes linear regression with single parameters, linear regression with multiple parameters, Octave Tutorial, Logistic Regression, regularization, neural network, design of the

Linear regression is prone to problems of fitting or less fitting.Local weighted linear regression is a non-parametric learning method, when the new samples are predicted, the new weights are re-trained, and the values of the parameters are obtained by retraining the sample data, each time the parameter value of the pr

1 What is linear regressionThe relationship between the dependent variable and several independent variables is determined, and the linear relation model is constructed to predict the dependent variable2 Linear regression principleLeast squares OLS (ordinary learst squares)The minimum squared error between the Y and th

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