svm

问题 I am working on a project about the feedforward pathway of the ventral stream, and i have 6 images to be recognized at the InferoTemporal Layer. Please can someone give me images' exmamples showing to me what is the difference between training images and test images. So what i should add to my folder that contain my training images? Does i should add another folder that contain a list of test images ? if yes, what should be these test images? Does the training images must contains the images

问题 My data generated strange results with svm on R from the e1071 package, so I tried to check if the R svm can generate same result as WEKA (or python), since I've been using WEKA in the past. I googled the question and found one that has the exact same confusion with me but without an answer. This is the question. So I hope that I could get an answer here. To make things easier, I'm also using the iris data set, and train a model (SMO in WEKA, and svm from R package e1071) using the whole iris

问题 To understand well the concept behind how Support Vector Machine works in MATLAB, I advised you to read briefly THIS VERY IMPORTANT LINK. I am trying to apply the same concept but I have different train set and test set. For more precision: My train set refers to be named as C2res{1} where this last is equal to: C2res{1} = 1.0e-05 * Columns 1 through 10 0.5341 0.5822 0.6185 0.7555 0.7369 0.7131 0.5985 0.6483 0.5668 0.6620 Columns 11 through 12 0.6523 0.6097 My test set refers to be named as

问题 I have a set of data that I am trying to learn using SVM. For context, the data has a dimensionality of 35 and contains approximately 30'000 data-points. I have previously trained decision trees in Matlab with this dataset and it took approximately 20 seconds. Not being totally satisfied with the error rate, I decided to try SVM. I first tried svmtrain(X,Y) . After about 5 seconds, I get the following message: ??? Error using ==> svmtrain at 453 Error calculating the kernel function: Out of

问题 I have a set of data that I am trying to learn using SVM. For context, the data has a dimensionality of 35 and contains approximately 30'000 data-points. I have previously trained decision trees in Matlab with this dataset and it took approximately 20 seconds. Not being totally satisfied with the error rate, I decided to try SVM. I first tried svmtrain(X,Y) . After about 5 seconds, I get the following message: ??? Error using ==> svmtrain at 453 Error calculating the kernel function: Out of

问题 Lets say my feature vector is (x1, x2, ...xn) Could anyone give me a code to train one-class SVM using libSVM? How should I learn the parameters using cross validation. 回答1: This may help you label=ones(Number Of your training instances,1); % You should generate labels for your only class! model = svmtrain( label, Training Data , '-s 2 -t 2 -n 0.5' ) ; % You can change the parameters [predicted_label,accuracy]=svmpredict(TestLabels,Test Set, model); 来源： https://stackoverflow.com/questions

问题 I'm using libsvm (executables provided in the package.) for a 2-class classification problem. In training step I'm setting b parameter as 1, which is defined as; -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) and in predicting step I'm setting b parameter as 1 again, which is defined as; -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported But based on the

问题 I'm using libsvm (executables provided in the package.) for a 2-class classification problem. In training step I'm setting b parameter as 1, which is defined as; -b probability_estimates : whether to train a SVC or SVR model for probability estimates, 0 or 1 (default 0) and in predicting step I'm setting b parameter as 1 again, which is defined as; -b probability_estimates: whether to predict probability estimates, 0 or 1 (default 0); for one-class SVM only 0 is supported But based on the

问题 I have installed open cv 3.0 then i installed libsvm.Then i addded that to my pycharm packages.But still when typing this code svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 ) it shows this error svm_params = dict( kernel_type = cv2.SVM_LINEAR, AttributeError: 'module' object has no attribute 'SVM_LINEAR' i tried import libsvm but it didn't work.please help. Is opencv 3.0 is ok should i go back to 2.4? 回答1: You are accessing SVM_LINEAR with cv2

问题 I have installed open cv 3.0 then i installed libsvm.Then i addded that to my pycharm packages.But still when typing this code svm_params = dict( kernel_type = cv2.SVM_LINEAR, svm_type = cv2.SVM_C_SVC, C=2.67, gamma=5.383 ) it shows this error svm_params = dict( kernel_type = cv2.SVM_LINEAR, AttributeError: 'module' object has no attribute 'SVM_LINEAR' i tried import libsvm but it didn't work.please help. Is opencv 3.0 is ok should i go back to 2.4? 回答1: You are accessing SVM_LINEAR with cv2