We present a detection scheme for diffusing-wave spectroscopy (DWS) based on a two-cell geometry that allows efficient ensemble averaging. This is achieved by putting a fast rotating diffuser in the optical path between laser and sample. We show that the recorded (multispeckle) correlation echoes provide an ensemble averaged signal that does not require additional time averaging. Furthermore, c...

We compare two regularization methods which can be used to improve the generalization capabilities of Gaussian mixture density estimates. The rst method consists of deening a Bayesian prior distribution on the parameter space. We derive EM (Expectation Maximization) update rules which maximize the a posterior parameter probability in contrast to the usual EM rules for Gaussian mixtures which ma...

Bootstrap samples with noise are shown to be an eeective smoothness and capacity control technique for training feed-forward networks and for other statistical methods such as generalized additive models. It is shown that noisy bootstrap performs best in conjunction with weight decay regularization and ensemble averaging. The two-spiral problem, a highly non-linear noise-free data, is used to d...

It is well known fact that organizing different predictors in an ensemble increases the accuracy of prediction of the time series. This paper discusses different methods of integration of predictors cooperating in an ensemble. The considered methods include the ordinary averaging, weighted averaging, application of principal component analysis to the data, blind source separation as well as app...

One of the most relevant task in Sentiment Analysis is Polarity Classification. In this paper, we discuss how to explore the potential of ensembles of classifiers and propose a voting mechanism based on Bayesian Model Averaging (BMA). An important issue to be addressed when using ensemble classification is the model selection strategy. In order to help in selecting the best ensemble composition...

The ability to extract the mean of features from a rapidly viewed, heterogeneous array of objects has been demonstrated for a number of different visual properties. Few studies have previously investigated the rapid averaging of color; those that did had insufficient stimulus control or inappropriate methods. This study reports three experiments that directly test observers' ability to extract ...

Electroosmotic flows in axisymmetric capillaries with random zeta potentials were shown in [1] to exhibit axial velocity fluctuations about the mean plug flow. In this note we address the calculation of Taylor dispersion in such flows, and highlight the important differences between cross-sectional averaging (as usually used for dispersion problems) and ensemble averaging (as used in [1]).

Ensemble learning schemes have shown impressive increases in prediction accuracy over single model schemes. We introduce a new decision forest learning scheme, whose base learners are Minimum Message Length (MML) oblique decision trees. Unlike other tree inference algorithms,MMLoblique decision tree learning does not over-grow the inferred trees. The resultant trees thus tend to be shallow and ...

An extension of the modal overlap factor is introduced that includes ensemble variations and frequency averaging of plates and acoustic spaces. Uniformly and normally distributed variations of geometric and material parameters are considered. Using this modal overlap factor, approximate formulas are derived to identify the limit of predictability for the combined case of damping, ensemble varia...