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EM algorithm (two)-an approach to the algorithm

Introduction of EM algorithmIn one of the EM algorithm-the problem is introduced in the issue of the coin, the object function of the model, mentioned that the maximum likelihood estimation of the implicit variable to be solved with the EM algorithm,

Machine Learning Public Course notes (5): Neural Network (neural networks)--learning

This chapter may be the most unclear part of Andrew Ng's story, why do you say so? This chapter focuses on the post-propagation (backpropagration, BP) algorithm, Ng spends more than half time talking about how to calculate the error term $\delta$,

Summary of the principle of logistic regression

Logistic regression is a classification algorithm which can deal with two-tuple classification and multivariate classification. Although its name contains "regression" two words, but not a regression algorithm. So why is there a misleading word for "

"CS229 Note one" supervised learning, linear regression, LMS algorithm, normal equation, probabilistic interpretation and local weighted linear regression

Supervised learningFor a house price forecasting system, the area and price of the room are given, and the axes are plotted by area and price, and each point is drawn.Defining symbols:\ (x_{(i)}\) represents an input feature \ (x\).\ (y_{(i)}\)

EM algorithm (two)-an approach to the algorithm

Introduction of EM algorithm In one of the EM algorithm-the problem is introduced in the issue of the coin, the object function of the model, mentioned that the maximum likelihood estimation of the implicit variable to be solved with the EM

[Reference] various curve equation sets of Pro/E

1. disc spring Column Coordinate Equation: r = 5 Theta = T * 3600. Z = (sin (3.5 * theta-90) + 24 * t 2. Linear line. Playing the card Equation: A = 10 X = 3 * a * t/(1 + (t ^ 3 )) Y = 3 * a * (t ^ 2)/(1 + (t ^ 3 )) 3. helical

LDA of the text subject model (iii) The variational inference EM algorithm for LDA solution

The model of text subject LDA (i) LDA FoundationThe model of the text subject LDA (ii) The Gibbs sampling algorithm for LDA solutionLDA of the text subject model (iii) The variational inference EM algorithm for LDA solutionThis article is the third

Statistical learning Method Hangyuan Li---The 9th Chapter EM algorithm and its generalization

The 9th Chapter EM algorithm and its generalization EM algorithm is an iterative algorithm, which is used for maximum likelihood estimation of probabilistic model parameters with implicit variables (hidden variable), or maximal posteriori

Spoon inevitably touches the pot along, not tongue how to read theta and d?

In English phonetics, the hardest thing for Chinese to learn is today's pair, theta and D. It looks strange, what a good side to do, speak to the tongue out, not a bit beautiful greasy. Indeed, in general, this tone has to be used to bite the tip of

EM Algorithm Learning notes

Recently contacted with the PLSA model, it is necessary to use the desired maximization (expectation maximization) algorithm to solve the problem because the subject is introduced as an implicit variable in the model.Why an EM algorithm is

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