In this paper, we propose and validate a computationally efficient non-iterative domain decomposition procedure for calculating bivariate cubic L1 smoothing splines. This domain decomposition procedure involves calculating local L1 smoothing splines individually on overlapping “extended subdomains” that cover the global domain and then creating the global L1 smoothing spline by patching togethe...

In certain contexts, maximum entropy (ME) modeling can be viewed as maximum likelihood (ML) training for exponential models, and like other ML methods is prone to overfitting of training data. Several smoothing methods for ME models have been proposed to address this problem, but previous results do not make it clear how these smoothing methods compare with smoothing methods for other types of ...

The efficient transmission of variable-bit-rate (VBR) video streams is complicated by the burstiness that video compression standards such as MPEG introduce. Most of the existing techniques concentrate on stored video traffic smoothing or real-time video traffic smoothing. However, there is a growing number of live video applications, such as video-casts of courses or television news, where man...

A novel adaptive smoothing method is proposed for noise removal and feature preservation. Unlike the previous methods, we adopt two proposed measures to detect discontinuities in an image. Spatial variance is employed to form a measure for detecting contextual discontinuities that can be used for feature preservation and control of the smoothing speed, and another measure is proposed to detect ...

The increasing availability of rich (panel) data provides many opportunities to test theories on consumption smoothing behaviour. At the same time, the informational requirements in terms of data and modelling are high and very context specific, thus requiring a filtering of essential explanatory ingredients. In this paper we show how conceptual and exploratory empirical analysis can contribute...

This paper describes an approach to text classification using language models. This approach is a natural extension of the traditional Naïve Bayes classifier, in which we replace the Laplace smoothing by some more sophisticated smoothing methods. In this paper, we tested four smoothing methods commonly used in information retrieval. Our experimental results show that using a language model, we ...

The asymptotic properties of smoothing parameter estimates for smoothing splines are developed. We consider a variety of estimates including Generalized Cross Validation, Generalized Maximum Likelihood, and more generally Type II ML estimates and the properties of the marginal posterior mode. Under the usual Sobolov space frequentist assumptions on the function to be estimated , consistency and...

This paper describes an approach to text classification using language models. This approach is a natural extension of the traditional Naïve Bayes classifier, in which we replace the Laplace smoothing by some more sophisticated smoothing methods. In this paper, we tested four smoothing methods commonly used in information retrieval. Our experimental results show that using a language model, we ...