This paper investigates the mean square error optimal timefrequency kernel for estimation of the Wigner-Ville spectrum of a certain class of nonstationary processes. The class of locally stationary processes have a simplified covariance structure which facilitates analysis. We give a formula for the optimal kernel in the ambiguity domain and conditions that are sufficient for the optimal time-f...

This paper analyzes the large sample of a varying kernel density estimator of the marginal density of a nonnegative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator.

This study proposes a multiple kernel learning (MKL)-based regression model for crude oil spot price forecasting and trading. We used a well-known trend-following technical analysis indicator, the moving average convergence and divergence (MACD) indicator, for extracting features from original spot prices. Additionally, we factored in the possibility that movements of target crude oil prices ma...

Abstract: This chapter explores the features and advantages of kernel-based localization. Kernel methods simplify received signal strength (RSS)-based localization by providing a means to learn the complicated relationship between RSS measurement vector and position. We discuss their use in self-calibrating indoor localization systems. In this chapter, we review four kernel-based localization a...

This paper presents a sequential estimation procedure for an unknown multivariate regression function. Observed regressors and noises of the model are supposed to be dependent and form sequences of dependent vectors and numbers respectively. Two types of estimators are considered. Both estimators are constructed on the basis of Nadaraya–Watson kernel estimators. First, sequential estimators wit...

Adaptive algorithms based on kernel structures have been a topic of significant research over the past few years. The main advantage is that they form a family of universal approximators, offering an elegant solution to problems with nonlinearities. Nevertheless these methods deal with kernel expansions, creating a growing structure also known as dictionary, whose size depends on the number of ...

Because of a lack of a priori information, the minimum mean-squared error predictor, the conditional expectation, is often not known for a non-Gaussian time series. We show that the nonparametric kernel regression estimator of the conditional expectation is mean-squared consistent for a time series: When used as a predictor, the estimator asymptotically matches the mean-squared error produced b...

Title of dissertation: ADAPTIVE KERNEL DENSITY APPROXIMATION AND ITS APPLICATIONS TO REAL-TIME COMPUTER VISION Bohyung Han, Doctor of Philosophy, 2005 Dissertation directed by: Professor Larry S. Davis Department of Computer Science Density-based modeling of visual features is very common in computer vision research due to the uncertainty of observed data; so accurate and simple density represe...

To identifying of the high yielding wheat lines with optimal characteristics and to determine the traits which could be used as the selection index for increasing of grain yield, 40 recombinant inbred lines derived from a cross between Norstar and Zagros cultivars were evaluated in a randomized complete block design with three replications. The measured characters consisted of peduncle weight, ...

As the number of vehicles in roads increases, information of traffic density becomes crucial to municipalities for making better decisions about road management and to the environment for reduced carbon emission. Here, the problem of traffic density estimation is addressed when there is continuous influx of vehicle data. First the traffic density is modeled by the clusters of the speed groups t...