Unanswered 'rbf-kernel' Questions

3

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0answers

72 views

Kernel and regularization parameter of James–Stein estimator

Consider a FIR model of the form $y= Ug_0+e$ with $e$ white noise with variance $\sigma^2$. We assume that we have collected N input-output measurements $y$ and $U$. The James–Stein estimator is ...

asked Jun 12 '18 at 9:46

Betelgeuse

1016 bronze badges

3

votes

1answer

630 views

Calculation of nu and gamma in one-class SVM with rbf kernel

I am using python sklearn's one-class svm classifier for anomaly detection. I would like to know can I accurately calculate the required value for nu and gamma for rbf kernel. Is there any equation or ...

svm python scikit-learn kernel-trick rbf-kernel

asked Dec 10 '17 at 10:37

nzck

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23 views

In the broadest sense, what is a “kernel”?

In MCMC sampling methods, a transition kernel, as found in Metropolis(/Hastings) algorithm, is the comparison of the likelihood of the current position and the likelihood of the proposed position. ...

svm kernel-trick rbf-kernel

asked Mar 24 at 18:19

jbuddy_13

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274 views

Why SVM with gamma='scale' for RBF kernel works so well?

The intuitive explanation for the gamma parameter of the RBF kernel in SVMs is the following: Intuitively, the gamma parameter ...

machine-learning python svm scikit-learn rbf-kernel

asked Dec 8 '20 at 20:31

Andreas K.

1315 bronze badges

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0answers

340 views

Kernel function with a feature space equipped with an inner product that is not the dot product

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 \...

kernel-trick rbf-kernel

asked Mar 21 '19 at 15:44

Daniel López

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1

vote

1answer

17 views

What is the difference between a covariance matrix created by an RBF kernel and a covariance matrix created by

I can't explain something simple to myself and it is probably a matter of vocabulary, I am not sure... If I create and random normal $Z \in \mathbb{R}^{3\times5}$, each row and column has a mean of 0. ...

covariance gaussian-process rbf-kernel

asked 2 days ago

deltaskelta

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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 ...

regression scikit-learn gaussian-process kernel-trick rbf-kernel

asked Feb 9 at 17:06

Tyrion

232 bronze badges

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vote

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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:...

classification svm gaussian-process kernel-trick rbf-kernel

asked Feb 8 at 9:51

orematasaburo

1111 bronze badge

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0answers

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 ...

regression machine-learning svm kernel-trick rbf-kernel

asked Feb 1 at 3:19

Elf Priestess

1655 bronze badges

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vote

1answer

43 views

Calculation of Lipschiz constant for Square Exponential kernel

I am working with the Kernel function and want to calculate bounds using the concept of Lipschitz continuity. I do understand that the SE kernel is continuous, smooth, and differentiable. Is there any ...

gaussian-process rbf-kernel

asked Dec 23 '20 at 6:34

Parikshit Pareek

112 bronze badges

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vote

0answers

28 views

Are radial basis kernels able to model interactions between predictors?

I have been doing research using Support Vector Regression for some time, especially using radial basis kernel, for predicting a response variable from a set of numeric predictors. As a consequence of ...

machine-learning svm interaction rbf-kernel radial-basis

asked Nov 17 '20 at 19:13

Mike.forest

113 bronze badges

1

vote

1answer

49 views

SVR with combination of kernels

I am a beginner, and I am looking for some advice regarding the use of Support Vector Regression (SVR) to model (or fit if you prefer) a trend. Before you suggest other methods, for a number of ...

machine-learning svm nonlinear-regression rbf-kernel

asked Oct 4 '20 at 17:43

Mash

111 bronze badge

1

vote

1answer

43 views

What is a natural way to define RKHS over mixed spaces (discrete and continuous)?

It is well known that given a kernel $k$ over any space $\mathcal{X}$, there is a corresponding RKHS (Reproducing Kernel Hilbert Space) associated with the kernel $k$. For example, Radial basis ...

gaussian-process kernel-trick mixed-type-data rbf-kernel

asked Aug 5 '20 at 4:38

randomprime

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0answers

62 views

How is a polynomial kernel with infinite degree different from RBF Kernel?

I was reading about polynomial and RBF Kernels. According to my understanding: Polynomial kernels with degree >1 map the non-linear data into a higher dimensional feature space. Data that aren't ...

svm kernel-trick rbf-kernel

asked Aug 1 '20 at 22:24

Aastha Jha

756 bronze badges

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109 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 ...

python scikit-learn rbf-kernel rbf-network

asked Feb 15 '20 at 5:23

DanielTheRocketMan

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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. ...

feature-selection data-transformation kernel-trick feature-engineering rbf-kernel

asked Dec 14 '19 at 23:43

Jan Musil

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vote

1answer

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 ...

svm transform rbf-kernel rbf-network

asked Nov 26 '19 at 21:01

Jan Musil

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0answers

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 ...

machine-learning classification svm rbf-kernel rbf-network

asked Nov 26 '19 at 16:12

Jan Musil

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vote

0answers

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. ...

r pca rbf-kernel

asked Nov 22 '19 at 12:05

MathIsLife12

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0answers

52 views

Possible error in evaluating kernel gradient in scikit-learn's GPR

Perhaps I am missing something very obvious, but in the standard kernels associated with scikit-learn's Gaussian process regression framework, the radial basis function (RBF), $$f = e^{-x^2/2l^2},$$ ...

python scikit-learn gaussian-process rbf-kernel

asked Mar 13 '19 at 21:50

Mathews24

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vote

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90 views

Use the features selected with RFE SVM linear for prediction of SVM rbf

I was wondering if the features selected with RFE with SVM linear kernel are still "good" features when we use a non linear model, like SVM rbf kernel. This comes in practice when you want to use SVM ...

svm feature-selection linear-model kernel-trick rbf-kernel

asked Oct 9 '18 at 20:47

rugrag

111 bronze badge

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43 views

Adding new center in an RBF network without memorizing previous training examples

Suppose we train an RBF by minimizing the LSE on a couple of training points and we are doing it incrementally in an online fashion. So basically we update the QR factorization using e.g. Givens ...

machine-learning online-algorithms radial-basis rbf-network rbf-kernel

asked Aug 30 '18 at 11:37

BS.

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134 views

Kernels property: integral of kernel product $\propto k(x,y)$

Let $k$ be a kernel function (symmetric and semi-positive definite function). Does the following relationship hold: $\int_{-\infty}^{+\infty}k(x,u)k(y,u) du \propto k(x,y)$ ? Or for what type of ...

gaussian-process kernel-trick integral rbf-kernel

asked Jun 16 '18 at 16:48

user_anon

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230 views

The gamma parameter (or kernel width) for RBF Gaussian kernel in kernel PCA

Is there any general way or rule of thumb of how to determine the kernel width for KPCA?

machine-learning pca dimensionality-reduction kernel-trick rbf-kernel

asked Mar 16 '18 at 4:03

Vickyyy

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vote

0answers

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 ...

r svm rbf-kernel

asked Oct 20 '14 at 11:41

GaidinD

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1

vote

0answers

440 views

How to select a radial basis function?

Currently I am investigating interpolation of 3D data with radial basis functions (RBF) and I am wondering that there are quite a few families of such (see table1 here). However, I cannot find any ...

interpolation radial-basis rbf-kernel

asked Jan 17 '14 at 10:44

mojovski

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0answers

13 views

RBF networks and epsilon-NN

Will an RBF network with a Window kernel correspond to an epsilon-NN classifier? I know that kNN only considers the k nearest data points while RBF uses all the data. How does this relate to epsilon-...

k-nearest-neighbour rbf-kernel radial-basis

asked Mar 16 at 22:43

MLNewbie

917 bronze badges

0

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0answers

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, ...

functional-data-analysis characteristic-function rbf-kernel levy-distribution unbounded-moments

asked Feb 17 at 0:41

www3

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1answer

30 views

Does radial basis function kernel has a coefficient?

I found there are two forms of RBF function. these is a coefficient before $\exp$ $$ k_{f}\left(x_{i}, x_{j}\right)=\sigma^{2} \exp \left(-\frac{1}{2 \ell^{2}} \...

scikit-learn gaussian-process rbf-kernel

asked Jan 27 at 3:25

Joey Gao

1011 bronze badge

0

votes

0answers

26 views

Kernel PCA stopping rule / criterion

I have been comparing dimensionality reduction methods, while performing KPCA, I didn't know what number of PCs I should retain, how many to take and decide : "there it is, this is the new ...

machine-learning pca dataset kernel-trick rbf-kernel

asked Jan 24 at 17:38

CH3OH

12 bronze badges

0

votes

0answers

11 views

Trying to use a custom kernel in SVR

I am very new to python and ML, I'm trying to customize a RBF kernel to consider Mahalanobis distance instead of Euclidean distance but I am encountering problems when I try to predict on new ...

rbf-kernel

asked Dec 30 '20 at 23:20

Camilla Zacche

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0answers

14 views

Use cases exist for the Silverman kernel

I am well aware of the usage of the Gaussian, the boxcar function, the Epanechnikov function and others for use cases as kernel density estimation, Gaussian processes and others. But I have never seen ...

kernel-smoothing rbf-kernel

asked Dec 4 '20 at 20:48

Make42

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0

votes

2answers

46 views

Relationship between structural or statistical properties and hardness of classification

I am trying to understand the relationship between structural or statistical properties of training dataset and hardness of classification in the context of binary classification with SVM using RBF ...

svm kernel-trick supervised-learning rbf-kernel radial-basis

asked Sep 24 '20 at 11:29

Omar Shehab

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0answers

23 views

heuristics for gamma in rbf kernel

My question is a follow-up to this question: SVM rbf kernel - heuristic method for estimating gamma. Basically, I want to find interesting values for gamma by first calculating the pairwise distance ...

machine-learning svm rbf-kernel

asked Jul 8 '20 at 17:46

mm523

237 bronze badges

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votes

0answers

18 views

Can I customize the kernel function?

I want to know whether I can customize the kernel function? For example, the polynomial kernel is defined as: $$ K(x,y) = (x^Ty+c)^d $$ Could I modify it to the following: $$ K(x,y) = (||x-y||_2)^d $$ ...

self-study kernel-trick kernel-smoothing rbf-kernel kernel-mean-embedding

asked Jul 2 '20 at 3:46

zhang

1

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0answers

13 views

kernel and mean function for series of function

my series for functions are type. $$f(x) = a \sin(x-b) , a \sim \mathcal{N}(-1,2) , b \sim \mathcal{N}(-0.5,1)$$ Can someone get me started how to model these functions with GP. I am confused about ...

gaussian-process rbf-kernel

asked Jun 5 '20 at 4:49

manifold

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70 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 ...

regression normal-distribution scikit-learn gaussian-process rbf-kernel

asked May 23 '20 at 22:40

bparikh

11 bronze badge

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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. ...

spectral-analysis rbf-kernel k-nearest-neighbour

asked May 3 '20 at 20:29

Gogis

1235 bronze badges

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451 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 ...

python variance gaussian-process kernel-trick rbf-kernel

asked Apr 2 '20 at 13:28

JohnDizzle

1012 bronze badges

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1answer

36 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 ...

svm kernel-trick rbf-kernel

asked Jan 2 '20 at 14:15

methus

476 bronze badges

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