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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 ]
Python: the KernelReg class for mixed data types in the statsmodels.nonparametric sub-package (includes other kernel density related classes), the package kernel_regression as an extension of scikit-learn (inefficient memory-wise, useful only for small datasets) R: the function npreg of the np package can perform kernel regression. [7] [8]
Thus, SVMs use the kernel trick to implicitly map their inputs into high-dimensional feature spaces, where linear classification can be performed. [3] Being max-margin models, SVMs are resilient to noisy data (e.g., misclassified examples). SVMs can also be used for regression tasks, where the objective becomes -sensitive.
In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models.
In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular, it is commonly used in support vector machine classification .
The kernel of a reproducing kernel Hilbert space is used in the suite of techniques known as kernel methods to perform tasks such as statistical classification, regression analysis, and cluster analysis on data in an implicit space. This usage is particularly common in machine learning.
Pages in category "Kernel methods for machine learning" The following 18 pages are in this category, out of 18 total. This list may not reflect recent changes. F.
In machine learning, this technique is also known as spectral regression. Clearly, kernel PCR has a discrete shrinkage effect on the eigenvectors of K', quite similar to the discrete shrinkage effect of classical PCR on the principal components, as discussed earlier.