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Everything about Kernel methods for vector output
Kernel methods are a well-established tool to analyze the relationship between input data and the corresponding output of a function. Kernels encapsulate the properties of functions in a computationally efficient way and allow algorithms to easily swap functions of varying complexity. Wikipedia →
🧠Start here: a 6-step reading path
- Read the overview: Kernel methods for vector outputA 5-minute orientation
- Pick up a classic book: Linear Algebra with ApplicationsThe most-reprinted book on the subject
- Go sideways: Bayesian interpretation of kernel regularizationA closely related topic
- Go sideways: Matrix regularizationA closely related topic
đź“– Definitions
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🗂️ The subject at a glance
🏆 Landmark papers (most cited)
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đź“š Books worth reading
Linear Algebra with ApplicationsGareth Williams · 1984
The Elements of Statistical LearningTrevor Hastie, Robert Tibshirani · 2001
Gaussian processes for machine learningCarl Edward Rasmussen · 2005
Neural networksSimon S. Haykin · 1994
Advances in Neural Networks – ISNN 2013Chengan Guo · 2013
Ranked by number of editions on Open Library, a rough measure of how lasting a book is.
đź”— Related topics
- Bayesian interpretation of kernel regularization Bayesian interpretation of kernel regularization examines how kernel methods in machine learning can be understood through the lens of Bayesian statistics…
- Matrix regularization In the field of statistical learning theory, matrix regularization generalizes notions of vector regularization to cases where the object to be learned…
- 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…
- Neural tangent kernel In the study of artificial neural networks (ANNs), the neural tangent kernel (NTK) is a kernel that describes the evolution of deep artificial neural networks…
- Gaussian process In probability theory and statistics, a Gaussian process is a stochastic process (a collection of random variables indexed by time or space), such 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…
- 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…
- Multi-task learning Multi-task learning (MTL) is a subfield of machine learning in which multiple learning tasks are solved at the same time, while exploiting commonalities…