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2. What is the fastest way to compute an RBF kernel in python?

   stackoverflow.com/questions/47271662

   Using sklearn.metrics.pairwise.rbf_kernel. sklearn provides a built-in method for direct computation of an RBF kernel: import numpy as np from sklearn.metrics.pairwise import rbf_kernel K = var * rbf_kernel(X, gamma = gamma) Run-time comparison

3. Why Gaussian radial basis function maps the examples into an...

   stackoverflow.com/questions/23581508

   Importantly, you are correct. If you have m distinct training points then the gaussian radial basis kernel makes the SVM operate in an m dimensional space. We say that the radial basis kernel maps to a space of infinite dimension because you can make m as large as you want and the space it operates in keeps growing without bound.

4. what is the algorithm for an RBF kernel matrix in Matlab?

   stackoverflow.com/questions/17690132

   Creating a Radial basis function kernel matrix in matlab Hot Network Questions As a departing consultant should I do a handover meeting with the new person?

5. Sklearn SVM custom rbf kernel function - Stack Overflow

   stackoverflow.com/questions/69932948

   I was creating a custom rbf function for the SVC class of sklearn as following: def rbf_kernel(x, y, gamma): dis = np.sqrt(((x.reshape(-1, 1)) - y.reshape(1, -1 ...

6. Designing a Kernel for a support vector machine (XOR)

   stackoverflow.com/questions/5998951

   In this case, we know that the RBF (radial basis function) kernel w/ a trained SVM, cleanly separates XOR. You can write an RBF function in Python this way: return NP.exp(-gamma * NP.abs(x - y)**2) In which gamma is 1/number of features (columns in the data set), and x, y are a Cartesian pair.

7. SVR hyperparameter selection and visualisation - Stack Overflow

   stackoverflow.com/questions/62810216

   You should use your training set for the fit and use some typical vSVR parameter values. e.g. svr = SVR(kernel='rbf', C=100, gamma=0.1, epsilon=.1) and then svr.fit(X_train,y_train). This will help us establishing where the issue is as you are asking where you should put the data in the code.

8. How do I speed up my RBF kernel function in my k-means algorithm

   stackoverflow.com/questions/69434009

   I am tring to implement the RBF Kernel Function for my kernel k-means alg. Here is my formula. And then I implement it with Numpy, but there's a two-layer for loop, and I'm thinking about how to turn it into a matrix operation. Because if I could do matrix operations, it would be a lot faster to process my 784-dimensional data.

9. svm - Optimal sigma for the RBF kernel? - Stack Overflow

   stackoverflow.com/questions/50125820

   For your SVM there is sigma and C. Hence, you perform an exhaustive search over the parameter space where each axis represents an parameter and a point in it, is a tuple of two parameter values (C_i, sigma_i). So, to perform it, you simply choose a set for C: {C_1,..., C_n} and for sigma: {sigma_1,..., sigma_n} and train and afterwards test it ...

10. How to tune `gamma` parameter of precomputed RBF kernel in Python...

    stackoverflow.com/questions/46672656

    how to tune parameters of custom kernel function with pipeline in scikit-learn; However, these two links show examples of using Sklearn's inbuilt chi2_kernel and rbf_kernel functions, while I am interested in writing my own Gram matrix kernel as shown in my minimum working example code below.

11. python - kernel GridSearchCV parameters - Stack Overflow

    stackoverflow.com/questions/64145366

    Svm = GridSearchCV(Svm, param_grid=parameters, cv=kf,verbose=10) In principle, you can search for the kernel in GridSearch. But you should keep in mind that 'gamma' is only useful for ‘rbf’, ‘poly’ and ‘sigmoid’. That means You will have redundant calculation when 'kernel' is 'linear'. The better way is to use a list of dictionaries ...