pattern recognition and machine learning github

IntroductionPattern recognition is a data-based discipline, so the same problem that all pattern recognition problems face is the random problem of data. The implementation of each method in pattern recognition is based on a specific set of data samples, but this sample set

1. Supervised learningRegression algorithms are often used in supervised learning algorithms, so before speaking about regression, the first to say that supervised learning.We have learned a lot of classifier design methods, such as Perceptron, SVM, and so on, their common feature is that according to a given class label samples, training learning machine, and th

encapsulation of the OpenCV package. More opencvdotnet Information Human Face detection algorithm jviolajones Jviolajones is a Java implementation of the human Face detection algorithm viola-jones and is capable of loading OPENCV XML files. Example code: http://www.oschina.net/code/snippet_12_2033 more Jviolajones information gesture Recognition Hand-gesture-detection Gesture

Under the limited sample, if the decision tree grows very large, the branches are many, then it is possible to cause the limited sample to be more sensitive to the chance or noise of sampling, which leads to the learning and thus the poor ability of the fan.First look at a picture,is a test using the ID3 algorithm to obtain the size of the decision tree and the training data and test data on the relationship between the correct rate, it is not difficu

theoretical knowledge of K-L transformationK-L transformation is another common feature extraction method besides PCA, it has many forms, the most basic form is similar to PCA, it differs from PCA in that PCA is a unsupervised feature transformation, and K-L transform can take different classification information and realize supervised feature extraction.According to the KL expansion theory in stochastic process, the stochastic process is described as a linear combination of numerous orthogonal

normal vector w is pointing to the R1, that is, all the sample x in R1 is on the positive side of the categorical surface H, so all the sample x in R2 is on the negative side of H,At this point, the linear discriminant function g (x) can be regarded as an algebraic measure of the distance from a certain point x to the classification plane h in the sample feature space, and the distance vector of the sample X feature point to H in the R1 is R, which is obtained according to the vector properties

the above three kernel functions, which is the smoothing parameter, which reflects the impact of a sample on the estimated range.The non-parametric estimation of the probability density function requires a sufficient number of samples, as long as there are enough samples to ensure convergence to any density function, but also so the computational and storage ratio is large, and the previous parameter estimation is more suitable for small sample cases, and if the density function has sufficient

node as the current node, and emptying the saved node directory, if the current node P1 is at the last level, jump to 7), otherwise l=l+1, jump to 2);7) to the current node P1 each sample XI use rules and second, the nearest neighbor judgment; As long as the sample XI of rule two is satisfied, it can be calculated without calculation D (X,XI), and compared with the nearest B currently obtained, if it is smaller than the current one, Then we think that we have found a sample more recent than the

We have developed a false news detector using machine learning and natural language processing, which has an accuracy rate of more than 95% on the validation set. In the real world, the accuracy rate should be lower than 95%, especially with the passage of time, the way the creation of false news will change. Because of the rapid development of natural language processing and

(GitHub 695 stars) Link: Https://github.com/facebookresearch/MUSE No.2 Deep-photo-styletransfer: Code and data for Deep photo Style Transfer, Cornell University Fujun Luan (GitHub 9747 stars) Link: https://github.com/luanfujun/deep-photo-styletransfer No.3 Face recognition: The simplest Python command line facial recogni