:17692process finished with exit code 0The result is quite unexpectedly, ps/xbox/pc three main host of the theme paste proportion actually close to 1:4:9.If it is reasonable, there are two reasons for this:
In the previous generation of host wars, Xbox360 was the winner. And the key is that there's cracked
Although the PC version is not as good as the main engine version, but the PC version is cheap Ah, many users ah. And the key is that there's cracked ⊙▂⊙
Unreasonable is also

the uncertainty of the parameter, but also choose the model itself. In the Bayesian perspective, we usually need to model a Prior Distribution in the model. For example, in the fitting process of polynomial curves, we should not only choose to determine the parameters of the model, we also need to establish a prior parameter, so it is easy to combine the Bayesian formula :. In formula (1.43), P (d | W) on

a greater log density, the density of each big friend and more use of real avatar2, log density and friend density, log density and whether the use of real avatar in the account authenticity given the conditions are independent3, the use of the real picture of the user than the use of non-real avatar user average has a greater friend densityDue to the existence of dependency between characteristic attributes, naive Bayesian classification can not sol

/
2.2 Test phase
Load the data from the training phase into memory, calculate the probability of the document in each category, and find the category with the greatest probability.
Three, Mr Analysis
Test data: Sogou Lab Http://www.sogou.com/labs/resources.html?v=1
The first step here is to turn all the doc

Compared to "dictionary-based Analysis," machine learning "does not require a large number of annotated dictionaries, but requires a large number of tagged data, such as:Or the following sentence, if its label is:Quality of service-medium (total three levels, good, medium and poor)╮ (╯-╰) ╭, which is machine learning, trains a model with a large number of tagged data

Code test Environment: hadoop2.4+mahout1.0Previous blog: mahout Bayesian algorithm Development Ideas (expansion) 1 and mahout Bayesian algorithm development Ideas (expansion) 2 the Bayesian algorithm in Mahout is analyzed to deal with the numerical data. In the previous two blogs, there was no processing of how to clas

1. Preparation:(1) Prior probability: Based on past experience and analysis of the probability, that is, the usual probability, in the full probability of the expression is "from the result of the fruit"(2) Posterior probability: refers to the probability of re-correcting after obtaining the "result" information, usually the conditional probability (but not all of the conditional probability is the posterior probability), in the

Conditional probability: P (x| YJoint probability: P (X, Y)Edge probability: P (X), P (Y).Joint probability = conditional probability * Edge probabilityThe inverse problem is usually solved with conditional probabilities.
Inverse problem refers to the problem that the cause should be reversed from the result;
A positive problem is the introduction of results from a cause.
The inverse problems are common:
Communication: According to the received signal conta

into into, get 1-3 shows:Figure 1-3 returning the data graphAccording to the shape, using the mathematical method to obtain the ROC curve area of 0.9222. Then use Weka to view the tool data, 1-4 shows:Figure 1-4 Weka Return Data。Resources:[1] Data mining using Weka (http://www.cnblogs.com/bluewelkin/p/3538599.html)[2]

independent. Naive Bayesian classification and Bayesian belief network based on Bayesian theorem of posterior probability. Bayesian belief networks allow the definition of class conditional independence between subsets of variables.k Nearest Neighbor taxonomy: distance-based classification algorithm, lazy learning met

Preface: Completely do not understand the data analysis, statistics also forget the almost small white began to learn data analysis. Read the "In-depth data analysis", the data

Bayesian Data Analysis: an actual
Example of effect 238
Bayesian reasoning: Summary and discussion. 241
(Workshop) r language 243
Additional reading. 249
Chapter 2: Mathematical manhunt --
Bigfoot and the least person
Multiplication equal to 253
11.1 how to average. 253
Simpson (Simpson) paradox. 254
Standard deviatio

data, and creating predictions. The simple point is to find out the same kind of attributes.Microsoft Naive Bayes: The Microsoft Naive Bayes algorithm is a Bayesian theorem-based classification algorithm provided by Microsoft SQL Server Analysis Services that can be used for predictive modeling.These algorithms are supported by a number of underlying algorithms,

article describes the Microsoft Linear regression analysis algorithm, the principle and the Microsoft Neural Network analysis algorithm, just like the focus is not the same, the Microsoft Neural Network algorithm is based on a certain purpose, using the existing data for "probing" analysis, focusing on

(regression, interpolation)2. Convex Optimization (global optimization, local optimality, constrained optimization)3. Integral (numerical integral, Analog integral)4. Symbolic calculation (base, equation, integral, differential)Eighth lecture, Random analysisThe characterization and research of uncertainty is an important aspect of financial research and analysis, and this paper introduces some knowledge of stochastic

to group cases in a dataset into clusters that contain similar characteristics. These groupings are useful when browsing data, identifying exceptions in data, and creating predictions. The simple point is to find out the same kind of attributes.Microsoft Naive Bayes: The Microsoft Naive Bayes algorithm is a Bayesian theorem-based classification algorithm provide

The 1th chapter introduces "free related ebook + accompanying code" this chapter first introduces the course is what, what characteristics, can learn what, content arrangement, need what foundation, is suitable to study this course and so on. Then we summarize the data analysis, so that we have a whole understanding of the meaning and function of data

(in the value of risks, credit risk)Nineth Lecture, statistical analysisStatistical analysis is the core of financial data analysis, this talk about the common statistical analysis methods, financial applications and Python implementation. 1. Normality test 2, Portfolio Optimization 3, principal component

Space Data Analysis and R language practicesBasic InformationOriginal Title: Applied spatial data analysis with RAuthor: pebesma, E. J.) Gemel-Rubio (Gómez-Rubio, V .)Translator: Xu Aiping Shu HongPress: Tsinghua University PressISBN: 9787302302353Mounting time:Published on: February 1, January 2013Start: 16Page number

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