Kernel refers to weighting functions used in non-parametric estimation techniques (such as kernel density estimation or kernel smoothing). DO NOT USE this tag for [kernel-trick] which is reserved for kernel methods in machine learning.

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How to calculate third variable dependence on x,y variables and visualise with heatmap using binning?

I have a dataframe with an X and Y column and a third column with an additional variable (let's call it "ABC"). I would like to create a heatmap that visualizes how ABC depends on the X and Y ...
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Kdensity magnitude issue [duplicate]

I encoutered a problem of interpretation for the kdensity plot that i use to see the empirical distribution of earnings btw entrepreneur and wageworker of my dataset. I do not understand, yet, why I ...
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Comparing two estimated distributions

I estimated the density function (adaptive kde) from two samples and the cdf using approxfun() and integrate(). Now, I would implement a ks test to know if the two distributions are similar. I guess I ...
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Combining text and non-text features

I am working on a binary classification problem using SVM. I am currently using ksvm in R (kernlab package). The input is a combination of text and scores. I would like to be able to use substring ...
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58 views

What is the long run variance?

How is long run variance in the realm of time series analysis defined? I understand it is utilized in the case there is a correlation structure in the data. So our stochastic process would not be a ...
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1answer
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Understanding “the kernel has zero mean”

I am trying to understand kernel density estimation and found the graphic below illustrating different kernel functions on Wikipedia. I have no trouble reconciling it with the two statements "the ...
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set SVM parameter range values for tuning [duplicate]

I am newbie to using svm for classification. I want to tune svm parameters by .TrainAutofunction in EmguCV. But I don't know what are the range(min-max value) of below parameters that I should give to ...
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A step of a proof regarding the Nadaraya-Watson estimator

Let the data be $(y_i , X_i) $ where $y_i$ is real valued and $X_i$ is a q-vector. The regression function for $y_i$ on $X_i$ is $g(x) = E(y_i | X_i = x)$, we can write this as: $$y_i = g(X_i) + ...
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What are “parts” in Haussler's definition of R-convolution kernels?

I have been reading about R-convolution kernels: http://citeseerx.ist.psu.edu/viewdoc/download?rep=rep1&type=pdf&doi=10.1.1.110.638. These important types of kernels are generalization of ...
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28 views

Valid result when adding two kernels with negative coefficient?

If $k_1$ and $k_2$ be a kernel in $ \mathbb{R}^n \times \mathbb{R}^n $. we know $k(x,z)=ak_1(x,z) + bk_2(x,z)$ (kernel addition) is still a valid kernel if $\: a,b \geq 0\,$ ($a,b$ is real numbers, ...
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Locally weighted regression VS kernel linear regression?

I am trying to make it clear the relationship of the listed three methods. According to my understanding kernel regression means : the weight vector W lies in the space spanned by training data. $$ ...
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Kernel methods in machine learning?

I am beginning to tackle geostatistics problems where I tried to apply kriging(gaussian processes) to interpolate demographical water drop. According to my understanding, kernel methods are something ...
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55 views

Getting the probability density kernel estimator with R

I am working on a density estimation project and I need to get an estimation of the density as well as an equation for the density estimator (and not the estimate). I am working with kernel ...
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What is the effective kernel for smoothing methods?

I'm learning different smoothing methods and the term "effective kernel" came up and I don't really understand it. By definition, for a smoothing method, the vector of estimates ...
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28 views

Is it meaningful to compute a radial kernel density estimate from 2D data?

I am working with 2D spatial data, $(X_i, Y_i),\; i=1, \cdots, N$. My current research requires estimating the density of the distances between those data points in each of the two dimensions. So ...
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37 views

What is the time complexity of binary classification of SVM?

One of the earliest solution to the SVM problem is SMO applied to dual form.What is the time complexity of SMO algorithm? What is the best known time complexity to solve SVM algorithm (non linear)?
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Differences and similarity between fuzzy sets and kernel functions

The concept of fuzzy membership function and kernels seems to be similar. Fuzzy however has a more elaborate theory of logic. It seems that kernels and fuzzy membership are used in different ...
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Good implementation of SVM with operator valued kernel

I've already come across the question Vector Valued SVM but the replies doesn't point to SVM with any Operator Valued Kernel. I understand that struct svm can solve the same by solving inference ...
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Recommended/estimated number of radial basis functions in RBFN

thank you for taking the time to read my question. I am attempting to make a Radial Basis Function Network to see if a relationship exists between input/output data that I have been collecting. I ...
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78 views

How do you transform a decision boundary in the angle kernel to the original space?

Say I have training data $S_n$ and each point is of the form $x = \langle x_1 , x_2 \rangle$ in the original space (i.e. $x^{(i)} \in \mathbb{R}^2$). I was considering the following kernel: $$ ...
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31 views

Covariance function to draw an inverse function

In a Gaussian Process (GP), we know that choice of the covariance function determines the shape of function that can be drawn from the GP. eg. Constant : $\sigma _{o}^{2}$ Draws constant function ...
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49 views

Correct arguments for svm() function in R

I'm looking to implement a linear and non-linear SVM in R but having some confusion over which argument to use in svm(). For the linear SVM I want to add in the penalty $\gamma$ for soft margin. This ...