Search results
Results From The WOW.Com Content Network
Python: the KernelReg class for mixed data types in the statsmodels.nonparametric sub-package (includes other kernel density related classes), the package kernel_regression as an extension of scikit-learn (inefficient memory-wise, useful only for small datasets) R: the function npreg of the np package can perform kernel regression. [7] [8]
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. [1]
The kernel of a reproducing kernel Hilbert space is used in the suite of techniques known as kernel methods to perform tasks such as statistical classification, regression analysis, and cluster analysis on data in an implicit space. This usage is particularly common in machine learning.
Output after kernel PCA, with a Gaussian kernel. Note in particular that the first principal component is enough to distinguish the three different groups, which is impossible using only linear PCA, because linear PCA operates only in the given (in this case two-dimensional) space, in which these concentric point clouds are not linearly separable.
The Data Plane Development Kit (DPDK) is an open source software project managed by the Linux Foundation. It provides a set of data plane libraries and network interface controller polling-mode drivers for offloading TCP packet processing from the operating system kernel to processes running in user space. This offloading achieves higher ...
A kernel smoother is a statistical technique to estimate a real valued function: as the weighted average of neighboring observed data. The weight is defined by the kernel, such that closer points are given higher weights. The estimated function is smooth, and the level of smoothness is set by a single parameter.
Linux API, Linux ABI, and in-kernel APIs and ABIs. The Linux kernel provides multiple interfaces to user-space and kernel-mode code that are used for varying purposes and that have varying properties by design. There are two types of application programming interface (API) in the Linux kernel: the "kernel–user space" API; and; the "kernel ...
Unsupervised multiple kernel learning algorithms have also been proposed by Zhuang et al. The problem is defined as follows. Let = be a set of unlabeled data. The kernel definition is the linear combined kernel ′ = =. In this problem, the data needs to be "clustered" into groups based on the kernel distances.