Search results
Results From The WOW.Com Content Network
The function : is often referred to as a kernel or a kernel function. The word "kernel" is used in mathematics to denote a weighting function for a weighted sum or integral . Certain problems in machine learning have more structure than an arbitrary weighting function k {\displaystyle k} .
Thus, in a sufficiently rich hypothesis space—or equivalently, for an appropriately chosen kernel—the SVM classifier will converge to the simplest function (in terms of ) that correctly classifies the data. This extends the geometric interpretation of SVM—for linear classification, the empirical risk is minimized by any function whose ...
For degree-d polynomials, the polynomial kernel is defined as [2](,) = (+)where x and y are vectors of size n in the input space, i.e. vectors of features computed from training or test samples and c ≥ 0 is a free parameter trading off the influence of higher-order versus lower-order terms in the polynomial.
Least-squares support-vector machines (LS-SVM) for statistics and in statistical modeling, are least-squares versions of support-vector machines (SVM), which are a set of related supervised learning methods that analyze data and recognize patterns, and which are used for classification and regression analysis.
Since the value of the RBF kernel decreases with distance and ranges between zero (in the infinite-distance limit) and one (when x = x'), it has a ready interpretation as a similarity measure. [2] The feature space of the kernel has an infinite number of dimensions; for =, its expansion using the multinomial theorem is: [3]
Uses the new Khronos SPIR-V 1.1 intermediate language which fully supports the OpenCL C++ kernel language. OpenCL library functions can now use the C++ language to provide increased safety and reduced undefined behavior while accessing features such as atomics, iterators, images, samplers, pipes, and device queue built-in types and address spaces.
In computer vision, the bag-of-words model (BoW model) sometimes called bag-of-visual-words model [1] [2] can be applied to image classification or retrieval, by treating image features as words. In document classification , a bag of words is a sparse vector of occurrence counts of words; that is, a sparse histogram over the vocabulary.
[2] [3] [4] This has enabled detailed comparisons between SVM and other forms of Tikhonov regularization, and theoretical grounding for why it is beneficial to use SVM's loss function, the hinge loss. [5]