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After recompiling a kernel binary image from source code, a kernel panic while booting the resulting kernel is a common problem if the kernel was not correctly configured, compiled or installed. Add-on hardware or malfunctioning RAM could also be sources of fatal kernel errors during start up, due to incompatibility with the OS or a missing ...
In nonparametric statistics, a kernel is a weighting function used in non-parametric estimation techniques. Kernels are used in kernel density estimation to estimate random variables ' density functions, or in kernel regression to estimate the conditional expectation of a random variable. Kernels are also used in time-series, in the use of the ...
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 general task of pattern analysis is to find and study general types of relations (for example clusters, rankings ...
For instance, Microsoft Windows on x86-64 uses the GS segment to point to the Thread Environment Block, a small data structure for each thread, which contains information about exception handling, thread-local variables, and other per-thread state. Similarly, the Linux kernel uses the GS segment to store per-CPU data.
Processor Control Region. Processor Control Region (PCR) is a Windows kernel mode data structure that contains information about the current processor. It can be accessed via the fs segment register on x86 versions, or the gs segment register on x64 versions respectively.
After a system has experienced an oops, some internal resources may no longer be operational. Thus, even if the system appears to work correctly, undesirable side effects may have resulted from the active task being killed. A kernel oops often leads to a kernel panic when the system attempts to use resources that have been lost. Some kernels ...
Kernel regression. In statistics, kernel regression is a non-parametric technique to estimate the conditional expectation of a random variable. The objective is to find a non-linear relation between a pair of random variables X and Y . In any nonparametric regression, the conditional expectation of a variable relative to a variable may be written:
Multivariate kernel density estimation. Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density estimation with improved statistical properties.