The nature of statistical learning theory
The nature of statistical learning theory
Nonlinear component analysis as a kernel eigenvalue problem
Neural Computation
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Kernel Methods for Pattern Analysis
Kernel Methods for Pattern Analysis
Generalized Discriminant Analysis Using a Kernel Approach
Neural Computation
Input space versus feature space in kernel-based methods
IEEE Transactions on Neural Networks
An introduction to kernel-based learning algorithms
IEEE Transactions on Neural Networks
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For a learning algorithm, especially a linear algorithm, it can usually be extended to its kernel version endowed with the power of extracting non-linear features. In this paper, we explore two key questions in the kernelization of an algorithm. The first is the existence of the kernel version of an algorithm. We propose a new method to determine whether an algorithm can be kernelized. It has the advantage that it is not limited by the specific form of the algorithm and shows an insight view of kernelization. The second question is how to kernelize an algorithm. We prove a kind of equivalence between two kernelization processes. Related details are also discussed.