# Questions tagged [kernel]

Kernel functions are a class of functions which transform the original data into a new space in which the classes of the data are easier to separate by a kernel algorithm.

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### SVM kernel for detecting if a substring appears in some given string

I'm trying to do the exercise in 16.1 in the book Understanding Machine Learning, Ben-David, et al. formulated as follows: Consider the task of learning to find a sequence of characters ("...
49 views

### Why linear kernel regression is equivalent to plain linear regression?

I am trying to understand either intuitively/geometricaly and/or mathematicaly why the followings are equivalent: Classic Ordinay Least Squares linear regression Linear-kernelized Ordinary Least ...
19 views

### kernel methods and parameter updates

Background information: (it might be helpful to read the first 5 pages of this:https://cs229.stanford.edu/summer2020/cs229-notes3.pdf before answering the question). I’m currently learning machine ...
32 views

### Derivative of a KernelRidge regression model based on Coulomb Matrix descriptor

I am trying to take analytical derivatives of a KernelRidge regression model that takes as input a Coulomb Matrix descriptor. A Coulomb Matrix is a way of representing a molecular structure basically ...
80 views

### Why don't we increase the parameter from 64 to 128 in this CNN model?

I'm looking at an example lab from a coursera course titled Intro to Tensorflow. In this CNN model, they're gradually increasing the no. of filters from 16 to 32 and then 64. Why don't we increase it ...
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### Epanechnikov kernel smoothing and Priestley-Chao (PC) kernel estimate

I wrote the Python code below to try to automate the application of kernel smoothing using the Epanechnikov kernel with a bandwidth of h = 0.4 calculating the Priestley-Chao kernel estimate of the ...
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### Is the transformation implied by a positive-type kernel well-defined?

I’ve been trying to get my head around the particularity of the Hilbert space that a positive-type (equiv. positive definite) kernel represents an inner product on, and was hoping for some help in ... 19 views

### How to choose the optimal PCA kernel

In a chemometrics application, I need to reduce the dimensionality of a spectral scan. The standard PCA is linear. Not sure if the data is. How do I choose the most optimal PCA kernel?
1 vote
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### A mathematician from the outside looking in

I am wondering if anybody could give a survey of applications of approximation theory to data science. One application I am familiar with are, for example, wavelet neural networks. Does anybody know ...
58 views

### Kernel ridge regression (KRR), accuracy scale?

What does a good range for the accuracy score look like for the KRR model? For example, RMSE produces a value between 0 and 1, where values closer to 0 represent better fitting models. What's the ...
79 views

### memory bound for kernel tricks in machine learning

Based on Andrew Ng's lecture on Kernel, you use training samples (referred as landmarks l) and use them during prediction to construct the higher dimensional representation of the given sample. This ...
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### why layer of dimension 1 is outputting image of size n

I am studying a model where landmarks from an image are calculated. The work comes from Convolutional Experts Constrained Local Model for 3D Facial Landmark Detection. I need to confirm why the ...
296 views

### What is custom SVM kernel?

What is custom kernel in the Support Vector Machine. How is it different from Polynomial kernel. How to implement a custom kernel. Can you provide a code to implement a custom kernel.
1 vote
199 views

### What are the benefits of using spectral k-means over simple k-means?

I have understood why k-means can get stuck in local minima. Now, I am curious to know how the spectral k-means helps to avoid this local minima problem. According to this paper A tutorial on ...
1 vote
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### Bayesian Linear Regression using the Kernel Trick vs Constructing features using Kernels as Prototypes

How different is it to do Bayesian linear regression using the GP approach (kernel trick) versus constructing features using kernels to prototypes? As far as I know, this very basic question is ...
155 views

### Kernel trick derivation: why this simplification is incorrect?

I am trying to derive kernel trick from linear regression, and I have a mistake in the very end, which leads to an expression too simple. Basic linear regression For a basic linear regression (with no ...
1 vote
70 views

### which type of machine learning algorithms perform better at extrapolation (in general)

Assuming that: the problem lies in the field of natural science, i.e. relationships between variables are physics-based and does not change depending on context its a regression based model Would it ...
1 vote
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### Support Vector Machine (SVM) for classification problem based on Earth Mover's Distance (EMD)

I would like to run SVM for my classification problem using the Earth Mover's Distance (EMD) as a distance measurement. As I understood the documentation for Python scikit-learn (https://scikit-learn....
139 views

### Many separation line using RBF kernel in SVM

Below is my code, it take a range of a number, creates a new column label that contains either -1 or 1. In case the number is ...
58 views

### What is a good method for detecting local minims and maxims?

I'm using kernel density estimation in order to compute probability density function for item occurrence. Using this output, i want to find all the local minims and maxims. I'm interested in different ...
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### Kernel engineering, valid kernels, multipliying by constant =0?

I am reading Bishop, Pattern Recognition and Machine Learning. In the chapter about kernels rules are given for constructing kernels from existing valid kernels. The first one being let k(x,y) be a ...
678 views

### What are practical differences between kernel k-means and spectral clustering?

I've been lately wondering about kernel k-means and spectral clustering algorithms and their differences. I know that spectral clustering is a more broad term and different settings can affect the ...
503 views

### Why spectral clustering results in disjointed cluster?

I'm working on a project where I have to dynamically cluster the position of objects with respect to one coordinate. So I'm essentially dealing with subsequent frames and each frame represents a one-...
126 views

### Implementing a Kernel Adaptive Filtering model explained in a paper

In this paper, Stock price prediction using kernel adaptive filtering within a stock market interdependence approach, the authors propose a method for predicting stock prices by combining the ...