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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Manifold vs Kernel [closed]

I'm currently reading Pattern Recognition and Machine Learning. I'm trying to get a good grasp on the differencs between a kernel and a manifold. I've read three papers outside of this book and it ...
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SVD Kernel and Linear Algebra Kernel, is there a conceptual difference?

Is the term kernel used in Sklearn to execute the SVD machine learning algorithm conceptually related to the notion of a kernel in linear algebra ( null space )? Or do they happen to use this same ...
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Has anyone succeeded in finding a good Scala/Spark kernel for Jupyter?

The ones I've tried so far Almond: Works very well for just Scala, but you have to import dependencies, and it gets tedious after a while. And unfortunately can't run when using Spark with YARN ...
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Kernel approximation of a function known only point-wise?

Assume that I have a set of $N$ points $x_i, i=1,...,N,$ in some space $\mathbb{R}^D$, and corresponding point-wise (scalar) function evaluations $f(x_i)$. It is my goal to approximate the unknown ...
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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 $H_0$ ...
19 views

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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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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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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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 ...
24 views

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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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 ...