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conditional random field prediction problem is the given condition with the airport p (Y | x) and the input sequence (observation sequence) x,The output sequence (marker sequence) with the most conditional probability is y*, that is, the observed sequence is labeled. According to the vector form of the conditional random field, the prediction problem of the conditional random field becomes the optimal path problem with the most non-normalized probability, which is solved by the Viterbi algorith

Ascending tree is an ascending method based on the classification tree or the regression tree as the basic classifier. Ascension Tree is considered to be one of the best performance methods in statistical learning.The lifting method actually adopts the addition model ( linear combination of the basis function ) and the forward Step-up algorithmA lifting method based on decision tree is called an ascending tree?Decision tree for classification p

classify the data in K class, instead of K-1 the total of two classes each time, we only need each weak classifier better than the random guessing (i.e. Accuracy rate >1/k)Multi-Class classification algorithm flow:Loss function design for multi-class classifiers:=============== Supplement ===============The ten algorithms of data mining can be studied slowly later:C4.5K-meansSvmAprioriEmPageRankAdaBoostKnnNaivebayesCART=============== Summary ===============Boosting can be used for variable sel

mistakenly classified data (x, y), there is-y (wx + B)> 0 (Buddha said: Too lazy to say ). Then there is a loss function (proving something to die ): 　　 Then the loss function is minimized (-_-zzz ): 　　 The perception machine learning algorithm is drive by mistake (the word "driven" sounds very powerful), and the Stochastic Gradient Descent Method (which will be written later ), evaluate the skewness for W and B respectively: 　　 After the request is

Regression Problemsregression (regression): input and output are continuous variables, used to predict the relationship between input variables and output variables, that is, the selection of input variables to the output variable mapping function, equivalent to function fitting, select function curve to fit the known data and good prediction of unknown data.According to the number of input variables, it is divided into unary regression and two-yuan regression; According to model type, it is di

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distributed. The probability of each sample is the same. 2. Obtain m weak classifiers through iterative learning. For the m weak classifier, 2.1 For the training set, the classifier, GM 2.2 calculates the weighted error of the weak classifier. 2.3 calculate the weight of the weak classifier. The smaller the visible error of the log function, the higher the weight, that is, the greater the role of the final strong classifier. 2.4 Key

for classification are conditionally independent under the conditions determined by the class. Naive Bayesian method actually learns the mechanism of generating data, so it belongs to the generation model. Naive Bayes method is a class decision based on the maximum posterior probability (MAP) criterion, and the posterior probability is the same as the denominator, and the classifier can be expressed as the maximum posteriori probability.equivalent to0-1 when the loss functionminimize the risk o

basket, scattered to multiple baskets can reduce the risk of egg fragmentation. When learning probability models, the model with the greatest entropy is the best model. The maximum entropy model is to select the largest model in the set of models satisfying the constraint conditions. Definition: The maximum entropy model is used to determine the most suitable classification model for U-Anze. The model with the most conditional entropy of

Python code implementation on the perception machine ----- Statistical Learning Method Reference: http://shpshao.blog.51cto.com/1931202/1119113 1 #! /Usr/bin/ENV Python 2 #-*-coding: UTF-8-*-3 #4 # Untitled. PY 5 #6 # copyright 2013 T-dofan There are still a few questions, the book's adjustment strategy is: Wi = wi + Nyi * Xi, so it is necessary to multiply the optimization process by X [2]? -----------

First, k nearest neighbor algorithmK-Nearest Neighbor method (K-nearest neighbor,k-nn) is a basic classification and regression method, the input instance of the eigenvector, the output instance of the category, where the category is preferable to multi-classSecond, K nearest neighbor model2.1 Distance MeasurementDistance definition:(1) When P=1, called Manhattan Distance(2) When p=2, called European distance(3) When p is infinitely large, it is the maximum value of each coordinate distance max|

estimate. General steps to find the maximum likelihood function estimate: (1), write out the likelihood function, (2), log likelihood function take logarithm, and arrange, (3), derivative number, make derivative 0, obtain likelihood equation , (4), solve likelihood equation, get parameter is to seek. Maximum (maximal) likelihood estimation is also an example of empirical risk minimization (RRM) in statistical l