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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KLMS in machine learning

As Least Mean Square is a very popular choice to be used in combination with neural networks topologies, what would be the most common machine learning algorithms (and easily) to combine with Kernel ...
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Bottleneck Distance

Is there a range of values for the bottleneck distance in persim package (python) to conclude that the two datasets are similar? Also, does it make sense to compute the bottleneck distance using $H_0$ ...
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Why do we use 2D kernel for RGB data?

I have recently started kearning CNN and I coukdnt understand that why are we using a 2D kernel like of shape (3x3) for a RGB data in place of a 3D kernel like of shape (3x3x3)? Are we sharing the ...
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PCA vs.KernelPCA: which one to use for high dimensional data?

I have a dataset which contains a lot of features (>>3). For computational reasons, I would like to apply a dimensionality reduction. At this point I could use different techniques: standard PCA ...
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Gaussian Process Classifier and specifying kernel

I am using scikitlearn's gaussian process classifier and either I don't think I understand how the kernel is used (more likely), or there is an error in the module (less likely). In short, the ...
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How do I create these Kernel functions in Python for Gaussian Process Regression?

I have a dataset of 1031 observed samples of 7 features that form the X and one target variable that forms the Y. I am using Gaussian Process Regressor to train my models. I want to use anisotropic ...
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How to create anisotropic exponential and gaussian correlation function in Python for kernel?

I have a dataset of 1000 observed samples of 6 features that form the X and one target variable that forms the Y. I am using kriging or Gaussian Process Regressor to train my models. I would like to ...
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9 views

Preparing L2 norm kernel to be use in SVR

I have a pandas DataFrame and I want to use SVR with one of DataFrame's columns ("Age"). In this SVR model, I want to use the L2 norm. To do this, I created a custom kernel function called ...
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Using kernel estimation to find similarity/difference between two feature sets for binary classification

I am trying to train a binary classifier using word vectors. I have the tfidf vectors for each sentence in my training set. Before applying binary classification algorithms, I just want to check ...
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32 views

How to use a RBF kernel to create a “Kernel Space” using the similarity of each pair of point?

I am trying to use Semi-Unsupervised clustering using reinforcement learning following this paper. Assume I have n data-points each of which has d dimensions. I also have c pairwise constraints of ...
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How to implement SVM from scratch?

I am trying to build a SVM from scrath and I would like to maximize this Lagrarian expression: I know what variables means but I would like to know how this maximization is implemeted. Should I start ...
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Kernel Density Estimation for bimodal distribution with Python

I have a bimodal distribution for the range [-0.1, 0.1] which can be viewed here: I want to train/fit a Kernel Density Estimation (KDE) on the bimodal distribution as shown in the picture and then, ...
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Understanding SVM Kernels

Following Andrew Ng's machine learning course, he explains SVM kernels by manually selecting 3 landmarks and defining 3 gaussian function based on them. Then he says that we are actually defining 3 ...
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1answer
45 views

How to choose a kernel function and a feature mapping function?

Although, after extensive of reading, I know the concepts of support vector machines pretty well by now, I have trouble translating the concept of the kernel function $K$ and the feature mapping ...
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GP derivative in GpyTorch

I am working on a project using GP-regression models to model transition and measurements models in a Kalman Filter. This means I need to be able to sample from the derivative of the original GP model....
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How do you prevent multiple kernels in a CNN from recognizing the same feature?

I've been reading Rosebrock's "Deep Learning for Computer Vision with Python", and he mentions that in a CNN, one of the layers is a set of $K$ kernels that each activate when they see a specific ...
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What is the kernel matrix used for in the kernel trick?

I have $n$ linearly inseperable datapoints, $x_1 \dots , x_n$. I use the kernel trick to map and compute the dot product in higher dimensions (without actually mapping / transforming the data). ...
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Generalized quadratic loss learning

I'm studying a binary classification task with an objective function, derived from SVM, defined so: $\vec{\xi}' S \vec{\xi}$ with: $y_i (f(\vec{x}_i)) >= 1 - \xi_i, i=1..l$ and: $\xi_i >=0,...
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Using rbf_kernel with two 2D numpy arrays causes Python 3 kernel to die

I am trying to calculate the maximum mean discrepancy between two datasets, X, Y, where the entries of X, Y are of type ...
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Deploy kernel from Github to Azure ML

Is there a way to have my kernel in Github and have an automatic deployment system to deploy the model to Azure ML with some tests after the deployment or in the time of deployment? I have read this ...
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1answer
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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....
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2answers
61 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 ...
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Code freezes and never returns when linear_kernel (sklearn.metrics.pairwise) is used on 20M Movielens dataset

I'm fairly new to ML/AI, i'm trying learn the content based recommendation - here is my source code - https://github.com/jaganlal/content-based-recommender I'm using MovieLens 20M dataset - tags.csv ...
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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 (triangles in the figure) occurrence. Using this output, i want to find all the local minims and maxims. ...
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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 ...
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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 ...
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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-...
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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 ...
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Gradient equations of gaussian kernel discriminant trained with gradiant descent

I am having a hard time trying to find the gradient equations for the weight $\alpha^t$ and $w_0$ for a gaussian kernel discriminant trained with gradient descent with the following error function $$E(...
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In SVM, is the support set still small if kernel trick is used?

In SVM, we classify y based on whether f(x) > 0 or f(x) < 0. I understand that in SVM with f(x) being linear in x, the support set is typically small (i.e., the number of support vectors is much ...
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Kernel Trick and Inner Product Preservation

I understand that the point of using the kernel trick is to project the problem onto a higher dimensional space, where the problem is linearly separable. In this explanation, https://www.quora.com/...
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how use RBF for primal model of svm?

I know if we want to solve primal model of non-linear SVM, we have to generate new features. for example for kernel (1+xz)^2 for primal problem for any pair of features x1 and x2 we have to generate: ...
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Designing Custom Kernel from my Mathematical model

I derived a mathematical model for a porous system and the final function looks like this , after going through the Mercers Theorem and its condition for a kernel I would love to write an SVM kernel ...
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525 views

Keras: Misunderstanding the Conv2D's param “filters”?

From https://keras.io/layers/convolutional/ (Conv2D): ...
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Does it matter whether we put regularization parameter ($C$) with error or weight term in Kernel ridge regression?

Kernel ridge regression associate a regularization parameter $C$ with weight term ($\beta$): $\text{Minimize}: {KRR}=C\frac{1}{2} \left \|\beta\right\|^{2} + \frac{1}{2}\sum_{i=1}^{\mathcal{N}}\left\|...
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226 views

Kernel PCA and K largest eigenvectors

How can one prove that the optimal kPCA solution $a^*=\{a_1...a_K\}$ are the $k$-largest Eigenvectors of the (centered) kernel matrix $K$? I referred to a lot of resources and couldn't find a proper ...
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Implementing SVM with Gaussian Kernel

This is referencing Prof. Andrew Ng's course on machine learning. In the part that details implementing an SVM with the Gaussian kernel, we are supposed to use all the training examples as our ...
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1answer
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Does increasing kernel size in a CNN result in higher accuracy on the training set?

In a convolutional neural network, does increasing the size of kernel always result in better training set accuracy? For example, if I use 5x5 kernels in a CNN instead of 3x3 ones, will it always ...
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Is there any conceptual relationship between 'kernel' in SVM and 'kernel' in convolution neural net?

In SVM, we have kernel function that maps an input raw data space into a higher dimensional feature space In CNN, we also have a 'kernel' mask that travels the input raw data space (image as a matrix)...
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Finding the kernel in which the data will have the highest ratio of leading eigenvalue to trace

I have a method which works well with the data where a ratio of a leading eigenvalue or of first few leading eigenvalues to trace is high. Is it possible to find a kernel transformation which is ...
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1answer
647 views

Computation of kernel matrix using radial basis kernel in svm

I want to compute a kernel matrix using RBF on my own. The training data is multidimensional. My query is whether we will apply $$e^{-\gamma(x-y)^2}$$ for each dimension and then sum the values across ...
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1answer
634 views

Does Convolution kernel size affect number of channels?

I am going through Dilated Residual Network blog post. In this, Under 2.Multi-scale Context aggregation heading, author mentioned this. The last one is the 1×1 ...
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1answer
45 views

Positive semidefinite kernel matrix from Gower distance

I have a dataframe with continuous and categorical variables and I want to obtain a kernel matrix for classification. The kernel matrix must be symmetric and positive semidefinite, so that no ...
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1answer
40 views

Basis expansion for regression using neural network?

I am trying to approximate a nonlinear function using a neural network. There are 3-4 input units. The network is struggling a bit to generalize the function outside the vicinity of the training data ...
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1answer
264 views

Channels in convolutional layer

I usually see convolutions performed over all the channels of the input. For example a $3x3$ kernel is really a $3x3xN$ kernel for a an input with $N$ channels, thus resulting in a single output ...
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CNN strategy in recognizing spinned images

I wrote my CNN code from scratch with some convolution kernels. But my CNN can't recognize flipped/spinned images correctly when there are only a few convolution kernels (3*3). My convolution kernels ...
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1answer
139 views

RBF kernel can classify two classes as in figure?

As you can see, I have some points (belonging to red and blue class), and I would to use an RBF kernel but I think that an RBF kernel can make points linearly separable only if they are located in ...
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How to understand features impact in a non linear case?

I give a simple example: I have a set of houses with different features (# rooms, perimeter, # neighbours, etc...), almost 15, and a price value for each house. The features are also quite correlated (...
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118 views

Mercer's Theorem importance

I understood that Mercer's Theorem extends the definition of kernels also for infinite input space. In Machine Learning realm our training set is always finite and hence the input space is always ...