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/s and above bit rate, the MPEG-2 is significantly better than the MPEG-1. The MPEG-2 is backward compatible, that is, all standard-compliant MPEG-2 decoder can also play the MPEG-1 video stream normally. MPEG-2 technology is also applied in the HDTV Transmission System. MPEG-2 light transport for DVD-Video, now most of the HDTV (hd TV) also uses MPEG-2 encoding, resolution up to 1920x1080. Because of the popularity of MPEG-2, originally prepared for HDTV MPEG-3 finally declared to give up. A MP

As early as itpub saw a SQL expert, like the puzzle, the following is a puzzle. I tried SQL Server to resolve this issue.With 1 points,5 points , 10 points , 25 points , 50 cents coins into one yuan, a total of several combinations of methods? SELECT'1*'+RTrim(A. Number)+'+5*'+RTrim(b. Number)+'+10*'+RTrim(c. Number)+'+25*'+RTrim(d. Number)+'+50*'+RTrim(E. Number) asresult from(Select Number fromMaster.db

, that is, {CI} is incorrect. 20>/B: represents a non-word boundary.Example:VaR regx = // bi ./;VaR rsw.regx.exe C ("Beijing is a beautiful city ");Result: The matching is successful. The RS value is {IJ}, that is, it matches IJ in Beijing. 21>/CX, matching a control character. For example,/cm matches a control-M orCarriage return. The value of X must be either a A-Z or a-Z. Otherwise, consider C asThe original 'C' characters. (The actual example must be supplemented) 21>/D: match a number chara

. The value of x must be one-a-Z or a-Z. Otherwise, c is treated as aThe literal ' C ' character. (Practical examples also need to be added) 21>/D: Matches a numeric character, equivalent to [0-9].Example:var regx=/user/d/;var rs=regx.exec ("user1");Result: Match succeeded, RS value is: {user1} 22>/D: Matches a non-numeric character, equivalent to [^0-9].Example:var regx=/user/d/;var rs=regx.exec ("UserA");Result: Match succeeded, RS value is: {UserA} 23>/F: Matches a page feed character. 24>/n:

nearest neighbor in the feature space) Most of the samples belong to a category, then the sample belongs to that category.IX, Naive BayesIn many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC). Naive Bayesian model originates from classical mathematics theory, has a solid mathematical foundation, and stable classification efficiency. At t

form training countData Set. This step is also called Guided Learning.Among the many classification models, the two most widely used classification models are decision tree model andNaive Bayes model(NaiveBayesianModel, NBC ). The decision tree model solves the classification problem by constructing a tree. First, a training dataset is used to construct a decision tree. Once the tree is established, it can generate a classification for unknown sample

new dataset that has changed the weight value to the lower-level classifier for training, and finally combine the classifier obtained each time as the final decision classifier. 8. KNN: K-Nearest Neighbor Classification K's recent neighbor (k-nearest neighbor, KNN) classification algorithm is a theoretically more mature method than cosine and one of the simplest machine learning algorithms. The idea of this method is to assume that most of the K samples in the feature space that are most simil

one of the simplest machine learning algorithms. The idea of this method is: if most of the K most similar samples in the feature space (that is, the most adjacent samples in the feature space) belong to a certain category, the sample also belongs to this category. IX,Naive Bayes Among the many classification models, the two most widely used classification models are decision tree model and Naive Bayes model (naive Bayesian model, NBC ). The naive Ba

(NAIVEBAYESIANMODEL,NBC). Naive Bayesian model originates from classical mathematics theory, has a solid mathematical foundation, and stable classification efficiency. At the same time, the NBC model has few parameters to estimate, less sensitive to missing data, and simpler algorithm. In theory, the NBC model has the smallest error rate compared to other classi

is trained each time is combined as the final decision classifier.Viii. Knn:k-nearestneighbor ClassificationK Nearest neighbor (k-nearest NEIGHBOR,KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms. The idea of this approach is that if a sample is in the K most similar in the feature space (that is, the nearest neighbor in the feature space) Most of the samples belong to a category, then the sample belongs to that category.IX, Naiv

security. Under the control of the U.S. Navy, GE bought the assets of the Marconi US subsidiary and teamed up with other US companies, including at-T, Westinghouse and others, to set up the US radio Company (Radio Corporation of America), which monopolized the US radio business.Figure 8. RCA chief David Sarnoff jumps for joy on Dec, 1953 upon being informed the FCC have approved the RCA/NBC color TV standa RD [4].Courtesy Http://farm3.static.flickr.c

each sample has a pre-defined class, determined by a property called a class label.A training data set is formed for the data tuples that are analyzed for modeling, which is also known as guided learning.In many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (NAIVEBAYESIANMODEL,NBC). The decision tree model solves the classification problem by constructing a tree.

feature space (that is, the nearest neighbor in the feature space) belong to a category, and the sample belongs to that category.9. Naive BayesIn many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC). naive Bayesian model originates from classical mathematical theory. Has a solid mathematical foundation, as well as stable classification e

feature space (that is, the nearest neighbor in the feature space) belong to a category, and the sample belongs to that category.9. Naive BayesIn many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC).naive Bayesian model originates from classical mathematics theory, has a solid mathematical foundation, and stable classification efficiency.

training, and finally the classifier that is trained each time is combined as the final decision classifier.Eight, knn:k-nearest neighbor classificationK Nearest neighbor (k-nearest NEIGHBOR,KNN) classification algorithm is a theoretically mature method and one of the simplest machine learning algorithms.The idea of this method is that if a sample is the most similar in the K in the feature space (that is, the nearest neighbor in the feature space)Most belong to a category, the sample also fall

In many classification models, the two most widely used classification models are decision tree (decision tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC). The decision tree model solves the classification problem by constructing a tree. First, the training data set is used to construct a decision tree, and once the tree is set up, it can generate a classification for the unknown sample. The use of decision tree models in classification

Nicolas Heymann, an analyst at Prudential Equity Group at the investment bank, believes that Google may acquire NBC Universal, which will become part of Google's media matrix together with YouTube. According to foreign media reports, GE will carry out the largest ever restructuring in the next four months. Analysts suggested that GE should sell its NBC Universal and GE Money wholly, and said Googl

points in advance to remove the small sample of the role of classification.5 advantages and disadvantages of support vector machine (SVM)Advantages of SVM:One, can solve the problem of machine learning in the case of small samples.Second, can improve the generalization performance.Thirdly, we can solve the problem of high dimension.Four, can solve the nonlinear problem.Five, can avoid the neural network structure choice and the local minimum point problem.Disadvantages of SVM:First, sensitive t

of the role of classification.5 advantages and disadvantages of support vector machine (SVM)Advantages of SVM:One, can solve the problem of machine learning in the case of small samples.Second, can improve the generalization performance.Thirdly, we can solve the problem of high dimension.Four, can solve the nonlinear problem.Five, can avoid the neural network structure choice and the local minimum point problem.Disadvantages of SVM:First, sensitive to missing data.Second, there is no universal

tree model) and naive Bayesian model (Naive Bayesian MODEL,NBC). Naive Bayesian model originates from classical mathematics theory, has a solid mathematical foundation, and stable classification efficiency. At the same time, the NBC model has few parameters to estimate, less sensitive to missing data, and simpler algorithm. In theory, the NBC model has the small