Hyperkernel Based Density Estimation
نویسندگان
چکیده
We focus on solving the problem of learning an optimal smoothing kernel for the unsupervised learning problem of kernel density estimation(KDE) by using hyperkernels. The optimal kernel is the one which minimizes the regularized negative leave-one-out-log likelihood score of the train set. We demonstrate that ”fixed bandwidth” and ”variable bandwidth” KDE are special cases of our algorithm.
منابع مشابه
Porting Hyperkernel to the ARM Architecture
This work describes the porting of Hyperkernel, an x86 kernel, to the ARMv8-A architecture. Hyperkernel was created to demonstrate various OS design decisions that are amenable to push-button verification. Hyperkernel simplifies reasoning about virtual memory by separating the kernel and user address spaces. In addition, Hyperkernel adopts an exokernel design to minimize code complexity, and th...
متن کاملWavelet Based Estimation of the Derivatives of a Density for m-Dependent Random Variables
Here, we propose a method of estimation of the derivatives of probability density based wavelets methods for a sequence of m−dependent random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for the such estimators.
متن کاملWavelet Based Estimation of the Derivatives of a Density for a Discrete-Time Stochastic Process: Lp-Losses
We propose a method of estimation of the derivatives of probability density based on wavelets methods for a sequence of random variables with a common one-dimensional probability density function and obtain an upper bound on Lp-losses for such estimators. We suppose that the process is strongly mixing and we show that the rate of convergence essentially depends on the behavior of a special quad...
متن کاملLinear Wavelet-Based Estimation for Derivative of a Density under Random Censorship
In this paper we consider estimation of the derivative of a density based on wavelets methods using randomly right censored data. We extend the results regarding the asymptotic convergence rates due to Prakasa Rao (1996) and Chaubey et al. (2008) under random censorship model. Our treatment is facilitated by results of Stute (1995) and Li (2003) that enable us in demonstrating that the same con...
متن کاملIdentification of Hazardous Situations using Kernel Density Estimation Method Based on Time to Collision, Case study: Left-turn on Unsignalized Intersection
The first step in improving traffic safety is identifying hazardous situations. Based on traffic accidents’ data, identifying hazardous situations in roads and the network is possible. However, in small areas such as intersections, especially in maneuvers resolution, identifying hazardous situations is impossible using accident’s data. In this paper, time-to-collision (TTC) as a traffic conflic...
متن کامل