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  2. Kernel method - Wikipedia

    en.wikipedia.org/wiki/Kernel_method

    Empirically, for machine learning heuristics, choices of a function that do not satisfy Mercer's condition may still perform reasonably if at least approximates the intuitive idea of similarity. [6] Regardless of whether k {\displaystyle k} is a Mercer kernel, k {\displaystyle k} may still be referred to as a "kernel".

  3. Feature hashing - Wikipedia

    en.wikipedia.org/wiki/Feature_hashing

    In machine learning, feature hashing, also known as the hashing trick (by analogy to the kernel trick), is a fast and space-efficient way of vectorizing features, i.e. turning arbitrary features into indices in a vector or matrix.

  4. Kernel methods for vector output - Wikipedia

    en.wikipedia.org/wiki/Kernel_methods_for_vector...

    In typical machine learning algorithms, these functions produce a scalar output. Recent development of kernel methods for functions with vector-valued output is due, at least in part, to interest in simultaneously solving related problems. Kernels which capture the relationship between the problems allow them to borrow strength from each other.

  5. Radial basis function kernel - Wikipedia

    en.wikipedia.org/wiki/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, it is commonly used in support vector machine classification. [1]

  6. Polynomial kernel - Wikipedia

    en.wikipedia.org/wiki/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 the similarity of vectors (training samples) in a feature space over polynomials of the original variables, allowing learning of non-linear models.

  7. Kernel perceptron - Wikipedia

    en.wikipedia.org/wiki/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 that employ a kernel function to compute the similarity of unseen samples to training samples. The algorithm was invented in 1964, [1] making it the first kernel classification learner. [2]

  8. How to trick deep learning algorithms into doing new things - AOL

    www.aol.com/trick-deep-learning-algorithms-doing...

    When developers don’t have a lot of training samples or access to very powerful servers, they use transfer learning to finetune a pre-trained deep learning model for a new task. How to trick ...

  9. Category:Kernel methods for machine learning - Wikipedia

    en.wikipedia.org/wiki/Category:Kernel_methods...

    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.