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BoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those sub-error class samples (such as to the sub-class of the weight of the sample to increase the value)Convex optimizationThe optimal value of a function is often solved in machine learning, but in general, the optimal value of any function is difficult to solve, but the glo

special value of 0, because 0 does not affect the value update of the LR classifier.The partial deletion of sample eigenvalues in training data is a tricky issue, and many documents are devoted to solving the problem, as it is too bad to lose the data directly, and the cost of re-acquisition is expensive. Some optional data loss processing methods include:-Use the mean value of the available features to fill the missing values;-use special values to ± true complement missing values, such as-1;-

That years. I learn the main contents of machine learning:1. Basic introduction to machine learning, getting started with machine learning; 2. Linear regression and logistic. XX Performance Prediction System. Intelligent interacti

finite but large quantities of t instead; second, using the bootstrapping method in statistics To generate new data based on existing data simulations.bootstrappingThe data sampled by Bootstrap is randomly averaged out in the original n data, recorded and then re-extracted, and then taken n times, the resulting data is statistically referred to as Bootstrap sample.BaggingThe method of bootstrap aggregation (BAGging) is to generate a series of differe

http://www.zhihu.com/question/20822481 know the user,non-paper, non-rationaleSpirit_dongdong,wildog,MT practices and others agree Agree @ Zhang Ziquan, add a little bit more. Look at the problem estimates, the subject may be Learning machine learning things, so there will be this problem. But as other people have pointed out, the two approaches are not quite com

samples from n samples that have been put back2. Set up a classifier on the full attribute of the N samples (cart,svm)3, repeat the above steps, the establishment of a m classifier4, the prediction of the use of voting methods to obtain resultsBoostingBoosting in training will give a weight to the sample, and then make the loss function as far as possible to consider those Sub-error class samples (such as to the sub-class of the weight of the sample to increase the Value)Convex optimizationThe

equal to 1.5789 (greater than 1 is not related, since this is the value of the density function and is used only to reflect the relative probability of each value).With this data, the gender classification can be calculated. P (Height =6| male) x p (weight =130| male) x P (foot Palm =8| male) x p (male)= 6.1984 x e-9 P (Height =6| female) x p (weight =130| female) x P (foot Palm =8| female) x P (female)= 5.3778 x e-4 It can be seen that the probability of a woman is nearly 10,

Datasets: Exposing datasets100+ interesting data sets for statistical data http://rs.io/100-interesting-data-sets-for-statistics/Data Set subreddit https://www.reddit.com/r/datasetsUCI Machine Learning Library http://archive.ics.uci.edu/ml/ information : From a personal bloghttp://www.cnblogs.com/hellochennan/p/5352110.htmlhttp://www.cnblogs.com/hellochenn

Similarity measurement in machine learning, Comparison of method summaryai lin 1 weeks ago (01-10) 876 ℃ 0 Reviews CangwuWhen classifying, it is often necessary to estimate the similarity metric between different samples (similarity measurement), which is usually done by calculating the "distance" (Distance) between samples. The method used to calculate the distance is very fastidious, even related to the

Documenting today's exploration of machine learning directions, the Unit's laboratory environment is comfortable to use. Praise.Record my every step in the field of machine learning to grow. This experimental material was taken from Mr. Lin Dague's Big Data analysis and machine