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Based on the literal Relevance Model of Baidu keyword search recommendation tool, this article introduces the specific design and implementation of a machine learning task. Including target setting, training data preparation, feature selection and filtering, and model training and optimization. This model can be extended to Semantic Relevance models, and the design and implementation of Search Engine releva

distance/edit distance), Jaccard Distance (Jaccard distance), Correlation coefficientdistance (correlation coefficient distance), information Entropy (information entropy), KL ( Kullback-leiblerdivergence KL divergence/relative Entropy relative entropy).Optimization (optimized):non-constrained Optimization (unconstrained optimization): Cyclic Variable Methods (variable rotation method), Pattern search Methods (pattern searching method), Variable Simplex Methods (variable simplex method), Gradie

Supervised learning (supervised learning): The reason to call supervised learning is because we tell the algorithm what we want to predict. The so-called supervision, in fact, is whether our intentions can directly influence the forecast results. Typical representatives: Classification (classification) and regression (regression).Unsupervised

Machine learning is divided into two major categories, supervised learning (supervised learning) and unsupervised learning (unsupervised learning). Supervised learning can be divided in

From:http://www.zhizhihu.com/html/y2009/410.html Machine learning is an interdisciplinary area of computer science and statistics, and R on machine learning consists of the following aspects:1) Neural Network (neural Networks): The Nnet packet performs a single hidden layer

and through E (y) = 0.138 mobile y) To Get X = (−2.8, −1.8, −0.8, 1.2, 4.2) and Y = (−0.028, −0.018, −0.008, 0.012, 0.042), from (4) Pearson Constraints From the above explanation, we can also understand Pearson's constraints: 1. Wired relationship between two variables2. The variable is a continuous variable.3. All variables conform to the normal distribution, and the binary distribution also conforms to the normal distribution.4. Two variables are independent. In practice

modelsGenerate model: infinite sample = = "probability density model = generation model = =" PredictionThe generation method is obtained by the data Learning Joint probability distribution P (x, y) and then the conditional probability distribution P (y| x) =p (x, y)/P (×) as the model for prediction. Such a method becomes a build method because the model represents a generation relationship that produces output y for a given input x . The observation

based on Chi-square test in 12.3.2 mllib12.4 SummaryThe 13th Chapter Mllib actual Combat drills-iris analysis13.1 Modeling InstructionsDescription and analysis target of 13.1.1 data13.1.2 Modeling Instructions13.2 Data preprocessing and analysisMicroscopic analysis of 13.2.1--a comparative analysis of mean value and variance13.2.2 Macroscopic analysis--calculation of the length of different kinds of properties13.2.3 removing duplicates--Determination of correlation coefficients13.3 relationship

Machine learning six--k-means Clustering algorithmThink about the common classification algorithms are decision tree, Logistic regression,SVM, Bayesian and so on. classification, as a supervised learning method, requires that the information of each category be clearly known beforehand, and that all categories to be categorized have a corresponding category. Howe

ensure reversible ( reversible Sufficient condition : matrix X columns linearly independent )In retrospect, our approach is to use iterative methods to find out the value of the cost function, and not to find the cost function. That is to say, whether the so-called optimal solution can be obtained, either by iteration or by other means, in line with the above conditions.But the reality of the data is not so ideal.If not reversible, how to solve?1, to seek pseudo-inverse (

. ClassificationLogistic regression (logistic regression), logistic regression is the corresponding algorithm under the classification task of linear regression.L2 Norm-logistic regression model:$$ Min_{\omega,c}\space\space\space\frac{1}{2}\omega^{t}\omega + c\sum_{i=1}^{n}log (E^{-y_{i} (X_{i}^{T}\omega + c)} + 1) $$L1 Norm-logistic regression model:$$ Min_{\omega,c}\space\space\space\vert\omega\vert_{1} + c\sum_{i=1}^{n}log (E^{-y_{i} (X_{i}^{T}\omega + c)} + 1) $$　　3. Integrated Learning1. R

Spark sreaming and Mllib machine learningOriginally this article is prepared for 5.15 more, but the last week has been busy visa and work, no time to postpone, now finally have time to write learning Spark last part of the content.第10-11 is mainly about spark streaming and Mllib. We know that Spark is doing a good job of working with data offline, so how does it behave on real-time data? In actual productio

Machine learning goals: Let machines learn to complete tasks through several instances. Statistics is a field that machine learning experts often study. The machine learning method is n

Summary of machine learning problems Category Name Keywords Supervised Classification Decision tree Information Gain Classification regression tree Gini index, Gini 2 Statistics, pruning Naive Bayes Non-parameter estimation, Bayesian Estimation Linear Discriminant Analysis Fishre identification, fe

In the previous section, we introduced the overall framework of supervised learning and the basic points, according to the total number of thinking, then we will introduce the corresponding algorithms. Today, let's take a look at the application of Bayesian theorem in machine learning. The main points of this chapter are:1. Bayes theorem;2. Bayes theorem in class

is, the distribution statistics of the numbers appear, and are the result of normalization to the 0~1 interval. That is, the horizontal axis represents the number, and the vertical is the percentage of the number that corresponds to the horizontal axis in the 1000 random numbers. If you do not use the normalized horizontal axis for numbers (Normed=false), the vertical axis indicates the number of occurrences. If normalization is not used--the

Statement:Machine learning series mainly records their own learning machine learning algorithms in the process of some references and summaries, including some of the content is reference books and reference blog.Directory: What are association rules The concepts that must be known in association rules

included. Limited to nouns, not perfect for adjectives and verbs.Lexical similarity based on corpus statistics:For example, we can infer the meaning of an unknown English word based on many words and contexts. Corpus statistics are also a similar process. The semantic of a word is counted by the corpus of the Internet. Or have the opportunity to wiki Wikipedia semantic analysis and so on.Word sense disambiguationAfter the semantics are calculated, th

Nine algorithms for machine learning---regressionTransferred from: http://blog.csdn.net/xiaohai1232/article/details/59551240Regression analysis is to quantify the size of the dependent variable affected by the independent variable, to establish a linear regression equation or a nonlinear regression equation, so as to predict the dependent variable, or the interpretation of the dependent variable.The regress

Brief introductionMost of the text classification methods use model-based classification, which can be divided into two main categories: 1 based on the rule classification method, the classification rules are determined for each category of the class set, then the text is classified according to the category template, and the category of the text is determined. The rules based text classification methods include: Decision tree, association rule and Rough set, etc. 2 based on the statistical clas