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Everything about Polynomial kernel
Machine learning kernel function
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 in a feature space over polynomials of the original variables, allowing learning of non-linear models. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Polynomial kernelA 5-minute orientation
- Skim a recent paper: Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimizationInfluential work since 2022
- Pick up a classic book: Quasi-kernal polynomials and convergance results for quasi-minimal residual iterationsThe most-reprinted book on the subject
- Read the landmark paper: The kernel polynomial methodCited 952 times
- Go sideways: Least-squares support vector machineA closely related topic
- Go sideways: Radial basis function kernelA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🗂️ The subject at a glance
🏆 Landmark papers (most cited)
The kernel polynomial method
A Polynomial Kernel for Proper Interval Vertex Deletion
Using Polynomial Kernel Support Vector Machines for Speaker Verification
Modeling interactive components by coordinate kernel polynomial models
Multivariate Local Polynomial Kernel Estimators: Leading Bias and Asymptotic Distribution
A Randomized Polynomial Kernel for Subset Feedback Vertex Set
Universal Consistency of Local Polynomial Kernel Regression Estimates
Some sets of orthogonal polynomial kernel functions
🆕 Influential since 2022
Supervised kernel principal component analysis-polynomial chaos-Kriging for high-dimensional surrogate modelling and optimization
Topological Spin Excitations in Non-Hermitian Spin Chains with a Generalized Kernel Polynomial Algorithm
Identification of the Silverbox benchmark using extended polynomial kernel-based nonlinear observer canonical models
Stochastic analysis of structures under limited observations using kernel density estimation and arbitrary polynomial chaos expansion
Sum-of-Squares Hierarchies for Polynomial Optimization and the Christoffel--Darboux Kernel
📚 Books worth reading
- 📕Quasi-kernal polynomials and convergance results for quasi-minimal residual iterationsRoland W. Freund · 1992
- 📕Construction of a family of positive kernels from Jacobi polynomialsRahman, M. · 1974
- 📕Computing the roots of complex orthogonal and kernel polynomialsPaul E. Saylor · 1986
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
🔗 Related topics
- Least-squares support vector machine Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM)…
- Radial basis function kernel In machine learning, the radial basis function kernel, or RBF kernel, is a popular kernel function used in various kernelized learning algorithms. In particular…
- Support vector machine In machine learning, a support vector machine (SVM) or support vector network is a supervised max-margin model with associated learning algorithms that…
- Kernel method In machine learning, kernel machines are a class of algorithms for pattern analysis, whose best known member is the support-vector machine (SVM). These…
- Hinge loss In machine learning, hinge loss is a loss function used for training classifiers. Hinge loss is used for "maximum-margin" classification, most notably…
- Random feature Random features (RF) are a technique used in machine learning to approximate kernel methods, introduced by Ali Rahimi and Ben Recht in their 2007 paper…
- Relevance vector machine In mathematics, a Relevance Vector Machine (RVM) is a machine learning technique that uses Bayesian inference to obtain parsimonious solutions for regression…
- Regularized least squares Regularized least squares (RLS) is a family of methods for solving the least-squares problem while using regularization to further constrain the resulting…