# Questions tagged [rbf-kernel]

The RBF kernel, i.e., radial-basis-function kernel, occurs in the context of kernel methods in machine learning.

72 questions
Filter by
Sorted by
Tagged with
34 views

35 views

### About Gaussian kernel for distances other than Euclidian

I have a question about Gaussian kernel. I read the following site. https://datascience.stackexchange.com/questions/17352/why-do-we-use-a-gaussian-kernel-as-a-similarity-metric My question is whether ...
21 views

### What are my options for fitting a model with unbounded moments

I'm trying to fit a model on a distribution that likely has unbounded moments. Say it's some stable distribution generated from a linear combination of random shocks with unbounded at least variance, ...
37 views

### How to use sklearn's Gaussian Process Regression parameters?

I have been trying to play around with Gaussian process Regression. I have constructed a fake 1D data for this. I am using a Squared exponential kernel. I solved the regression problem using inbuilt ...
19 views

### How to extend kernel-based classifier to non-euclidean space like SO3

What is the proper way to extend kernel-based classifier to non-euclidean space like SO3? This kind of situation happens a lot in robotics, where the data points all live in a specific manifold. (Note:...
19 views

### Can someone improve my feeble understanding of feature maps and kernels?

I am taking the course and the extent in which we've discussed feature maps and kernels is as follows: Given Obviously we cannot use linear regression. Instead we map it to a space where it becomes ...
26 views

16 views

### Applying different kernels to parts of a dataset and merging the output [duplicate]

I am trying to create a classifier using SVM on a dataset that is composed of 6 sets of data for each of my observations. When I train the SVM (rbf kernel), I get a better performance of the ...
68 views

### Using Gaussian Process Regression in scikit-learn

I have a simple dataset with multiple trials of position over time, and I'm trying to fit a Gaussian Process over it. Here's a plot of all the raw data (6180 data points): My goal is to fit a ...
285 views

### Heavy-tailed RBF kernel function

I'm trying to run a SVM regression on some data and I want to use ksvm from kernlab or svm ...
35 views

### Using RBF k-NN graph in spectral clustering

In the article Spectral Clustering with Imbalanced Data there is mentioned usage of a "RBF k-NN" graph. I haven't encountered this kind of graphs before and couldn't google anything related to it. ...
450 views

### Calculate Gamma for RBF Kernel to get Gaussian Kernel

In order to measure the information density like proposed in section 3.2 of this paper I need a symmetric positive definite Kernel function. For this purpose I want to use the Gaussian Kernel like ...
179 views

### hyperparameters optimisation with linear kernel

I want to conduct an SVM model-regression (i.e., support vector regression), using a linear kernel function. Does it make sense to perform a cross-validation hyperparameter optimization when the ...
104 views

### Cannot use SVM with RBF Kernel

I'm new in R. I have an original dataset with 25771 variables and 118 samples. I already performed feature selection and split the dataset into 70 30 so i have 82 samples in my training data and 36 in ...
108 views

### What is the role of length scale bound in sklearn Radial Basis Function

The radial basis function provided by SkLearn (reference) has two parameters: length scale and length scale bounds. I understand that the length scale controls the importance of the coordinates of the ...
63 views

### What is the expanded representation, $\phi(X)$, required to obtain the RBF kernel?

For the two-dimensional case, where $\boldsymbol X=[x_1, x_2]$ and its corresponding expanded represetation $\boldsymbol\phi(X)= [1, \sqrt2 x_1, \sqrt2 x_2, x_1^2, x_2^2, \sqrt2x_1x_2]$, we can ...
35 views

### Adaptive Gamma in RBF Kernel

The RBF Kernel is defined by $K(x,y)=\exp(-\gamma ||x-y||^2)$ Wouldnt it be better to find a suited gamma value for each dimension? $K(x,y)=\exp(-\sum_i \gamma_i * (x_i-y_i)^2 )$ This would add ...
1k views

### Why are random Fourier features efficient?

I am trying to understand Random Features for Large-Scale Kernel Machines. In particular, I don't follow the following logic: kernel methods can be viewed as optimizing the coefficients in a weighted ...
20 views

### Is it good idea to generate features from data points similarity comparison?

I know about polynomial features in machine learning, which can introduce nonlinearity to original dataset. I also heard about binning, which also allows us to create new features from existing ones. ...
35 views

### Is SVM RBF applied to both classes?

Lets say i have following 1D data (position on x), color is target class and I need a classifier which classifies green from red: I decided to use SVM. Data is clearly not linearly separable, so i ...
236 views

### Gaussian RBF vs KNN explanation

I was studying SVM ML alghorythm and I was wondering about solution for non-linear cases. As I understand it for know, SVM tries to find hyperplane or object in defined n-dimensional space, which ...
24 views

### Labeling KPCA in R

The function $\texttt{biplot}$ in R is very useful for creating visualizations when performing PCA. However, when performing kernel PCA in R, I cannot find a way to label the loadings on the graph. ...
142 views

### Gaussian processes Sum of RBF kernels vs single anisotropic RBF kernel [closed]

Say I have some two dimensional data, for which I am trying to fit a Gaussian process. In scikit-learn, I can build an RBF kernel as follows K=sklearn.gaussian_process.kernels.RBF(length_scale=0.1) + ...
66 views

### The inner product properties seem to clash with the RKHS property for RBF kernels. What is off?

By the reproducing kernel Hilbert space (RKHS) property, given a P.S.D. kernel function $\kappa:X\times X \rightarrow \mathbb R$, there exists a Hilbert space $H$ and a map $\phi:X\rightarrow H$ such ...
334 views

### RBF kernel mapping

I was reading that the Gaussian/RBF kernel maps its input onto the surface of normalized hypersphere. Our RBF kernel given by: $k(x,z) = exp(\frac{- ||x-z||^2}{2\sigma^2})$ Can anyone explain why ...
Premise: A function $K: \mathbb R^d \times \mathbb R^d \to \mathbb R$ is called a kernel function on $\mathbb{R}^d$ if there exists a Hilbert space $\mathcal{H}$ and a map $\phi: \mathbb R^d \... 1answer 104 views ### Fundamental understanding of Gaussian Process and their terminology [closed] I am new to this site as well as Machine learning, so kindly bear with me. I have been trying to understand Gaussian process and their implementation. Notation: 1) Let's say that the$\vec{x}\in ...
I have general form of Gaussian kernel $K(x,x')=\exp(-\|x-x'\|^{2})$ (just not considering $\sigma$). I tried to prove its positive definiteness via Gram matrix properties, but couldn't. Is there any ...