List of datasets in computer vision and image processing
Outline of machine learning
v
t
e
In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve using linear classifiers to solve nonlinear problems.[1] The general task of pattern analysis is to find and study general types of relations (for example clusters, rankings, principal components, correlations, classifications) in datasets. For many algorithms that solve these tasks, the data in raw representation have to be explicitly transformed into feature vector representations via a user-specified feature map: in contrast, kernel methods require only a user-specified kernel, i.e., a similarity function over all pairs of data points computed using inner products. The feature map in kernel machines is infinite dimensional but only requires a finite dimensional matrix from user-input according to the Representer theorem. Kernel machines are slow to compute for datasets larger than a couple of thousand examples without parallel processing.
Kernel methods owe their name to the use of kernel functions, which enable them to operate in a high-dimensional, implicit feature space without ever computing the coordinates of the data in that space, but rather by simply computing the inner products between the images of all pairs of data in the feature space. This operation is often computationally cheaper than the explicit computation of the coordinates. This approach is called the "kernel trick".[2] Kernel functions have been introduced for sequence data, graphs, text, images, as well as vectors.
Algorithms capable of operating with kernels include the kernel perceptron, support-vector machines (SVM), Gaussian processes, principal components analysis (PCA), canonical correlation analysis, ridge regression, spectral clustering, linear adaptive filters and many others.
Most kernel algorithms are based on convex optimization or eigenproblems and are statistically well-founded. Typically, their statistical properties are analyzed using statistical learning theory (for example, using Rademacher complexity).
^"Kernel method". Engati. Retrieved 2023-04-04.
^Theodoridis, Sergios (2008). Pattern Recognition. Elsevier B.V. p. 203. ISBN 9780080949123.
learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These methods involve...
learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular,...
Compute kernel, in GPGPU programming Kernelmethod, in machine learning Kernelization, a technique for designing efficient algorithms Kernel, a routine...
that the kernelmethod could essentially replace the Volterra series representation, although noting that the latter is more intuitive. This method was developed...
statistics, kernel density estimation (KDE) is the application of kernel smoothing for probability density estimation, i.e., a non-parametric method to estimate...
In machine learning, the kernel perceptron is a variant of the popular perceptron learning algorithm that can learn kernel machines, i.e. non-linear classifiers...
It allows ANNs to be studied using theoretical tools from kernelmethods. In general, a kernel is a positive-semidefinite symmetric function of two inputs...
statistics, kernel principal component analysis (kernel PCA) is an extension of principal component analysis (PCA) using techniques of kernelmethods. Using...
two variables, that is called the kernel or nucleus of the transform. Some kernels have an associated inverse kernel K − 1 ( u , t ) {\displaystyle K^{-1}(u...
data is well-supported) discriminative methods can have flexible criteria and yield better results. The Fisher kernel makes use of the Fisher score, defined...
machine learning, the kernel embedding of distributions (also called the kernel mean or mean map) comprises a class of nonparametric methods in which a probability...
Kernelmethods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate...
learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the...
In structure mining, a graph kernel is a kernel function that computes an inner product on graphs. Graph kernels can be intuitively understood as functions...
A kernel smoother is a statistical technique to estimate a real valued function f : R p → R {\displaystyle f:\mathbb {R} ^{p}\to \mathbb {R} } as the weighted...
adaptive kernel estimates. Davies and Ghahramani proposed Random Forest Kernel and show that it can empirically outperform state-of-art kernelmethods. Scornet...
{\displaystyle {\mathcal {H}}(R)} be a Reproducing kernel Hilbert space with positive definite kernel R {\displaystyle R} . Then lim n → ∞ tr [ K n R...
best known methods for constructing utilization distributions are the so-called bivariate Gaussian or normal distribution kernel density methods. More recently...
approximated as a sum over kernels, whose domain is the feature space. If this is done in a reproducing kernel Hilbert space, a kernelmethod can be a universal...
computing, a loadable kernel module (LKM) is an object file that contains code to extend the running kernel, or so-called base kernel, of an operating system...
machine learning, kernelmethods arise from the assumption of an inner product space or similarity structure on inputs. For some such methods, such as support...
Density estimation Kernel density estimation Multivariate kernel density estimation Time series Time series analysis Box–Jenkins method Frequency domain...