Everything about Kernel method
Class of algorithms for pattern analysis
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. The general task of pattern analysis is to find and study general types of relations 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. Wikipedia →
🧭 Start here: a 6-step reading path
- Read the overview: Kernel methodA 5-minute orientation
- Skim a recent paper: ECG-based emotion recognition using random convolutional kernel methodInfluential work since 2022
- Pick up a classic book: Kernel methods in computational biologyThe 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: Support vector machineA closely related topic
📖 Definitions
No dictionary entry for this exact phrase.
🏆 Landmark papers (most cited)
The kernel polynomial method
Kernel Method for Nonlinear Granger Causality
A novel kernel method for clustering
PET Image Reconstruction Using Kernel Method
Kernel energy method illustrated with peptides
A kernel gradient free (KGF) SPH method
A kernel-based subtractive clustering method
Kernel Current Source Density Method
🆕 Influential since 2022
ECG-based emotion recognition using random convolutional kernel method
Process Monitoring Using Kernel PCA and Kernel Density Estimation-Based SSGLR Method for Nonlinear Fault Detection
A Kernel-Independent Sum-of-Exponentials Method
A kernel derivative free SPH method
A Generalized Kernel Method for Global Sensitivity Analysis
📚 Books worth reading
Kernel methods in computational biologyBernhard Schölkopf, Koji Tsuda · 2004
Heat Kernel and Quantum GravityIvan G. Avramidi · 2000
Singular integrals in boundary element methods · 1998
Causal Inference from Statistical DataXiaohai Sun · 2008- 📕Mémoire du territoire de WalikaleKyamwami, Kihangi Prince · 2018
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)…
- 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…
- Polynomial kernel In machine learning, the polynomial kernel is a kernel function commonly used with support vector machines (SVMs) and other kernelized models, that represents…
- Kernel perceptron 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…
- 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…
- 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…
- 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…
- Low-rank matrix approximation Low-rank matrix approximations are essential tools in the application of kernel methods to large-scale learning problems. Kernel methods (for instance…