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1. Haier TrainingNow, we use haartraining.exe to train our classifier. The training statement is as follows: Usage:./haartraining-Data -VEC -BG [-NPOs [-Nneg [-Nstages [-Nsplits [-MEM [-Sym (default)] [-nonsym][-Minhitrate [-Maxfalsealarm [-Weighttrimming [-Eqw][-Mode [-W [-H [-BT [-Err [-Maxtreesplits [-Minpos Kuranov et. al. pointed out that 20*20 samples have the highest accuracy rate. In addition, the four split nodes have the

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When training with SVM: The problems that arise are:Error:specified Nu is infeasible The assignment dimension does not match with the subscript. Baidu to the answer: the assignment dimension does not match ... Indicates that the size of the equals sign is inconsistent, causing me to also run into this problem where the error occurred in Svmngforclass (line 34)CG (I,J) = Svmtrain (Train_label, train, cmd); Error Untitled3 (line 33)[Bestcvaccuracy,bestc

Fast partitioning of cross training datasets using Numpy.random.choice () and set () Before dividing the training set and validation set, are manually randomly generated index, very stupid. The new methods learned are as follows: Import NumPy as NP # Normal distribution generate raw data x = Np.random.random.normal (1,0.1,100) # Divide data by 8:2 X_train_index = Np.random.choice (len (x), round (len (x) *0

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