Applications of exponential smoothing to forecast time series usually rely on three basic methods: simple exponential smoothing, trend corrected exponential smoothing and a seasonal variation thereof. A common approach to select the method appropriate to a particular time series is based on prediction validation on a withheld part of the sample using criteria such as the mean absolute percentag...

Semi-supervised Support Vector Machines is an appealing method for using unlabeled data in classification. Smoothing homotopy method is one of feasible method for solving semi-supervised support vector machines. In this paper, an inexact implementation of the smoothing homotopy method is considered. The numerical implementation is based on a truncated smoothing technique. By the new technique, ...

This paper proposes a novel approach for smoothing surfaces represented by triangular meshes. The proposed method is a two-step procedure: surface normal smoothing through fuzzy vector median (FVM) filtering followed by vertex position update based on the least square error criteria. This paper applies the fuzzy ordering theory to the vector based data and introduces the fuzzy vector median. Th...

In this paper we propose a method for piecewise linear reconstruction and subsequent smoothing of a point sampled curve. The reconstruction step is based on the meshless parameterization reconstruction algorithm proposed by Floater. The information computed in the reconstruction step is used for a least squares based discrete smoothing method, with behavior comparable to a smoothing spline. We ...

A general version of multivariate smoothing splines with correlated errors and correlated curves is proposed. A suitable symmetric smoothing parameter matrix is introduced, and practical priors are developed for the unknown covariance matrix of the errors and the smoothing parameter matrix. An efficient algorithm for computing the multivariate smoothing spline is derived, which leads to an effi...

It is a common practice to complement a forecasting method such as simple exponential smoothing with a monitoring scheme to detect those situations where forecasts have failed to adapt to structural change. It will be suggested in this paper that the equations for simple exponential smoothing can be augmented by a common monitoring statistic to provide a method that automatically adapts to stru...

Smoothing noisy data with spline functions is well known in approximation theory. Smoothing splines have been used to deal with the problem of numerical differentiation. In this paper, we extend this method to estimate the fractional derivatives of a smooth signal from its discrete noisy data. We begin with finding a smoothing spline by solving the Tikhonov regularization problem. Then, we prop...

This paper proposes several approaches for NTCIR-12 SpokenQuery & Doc-2[1]. Our methods are based on the query likelihood model which is one of the probabilisrtic language models choosing Dirichlet smoothing. We try to improve the performance by using extended language models. First, this paper develops and uses the language model obtained from related research papers. Second, this paper propos...

The quadratically convergent algorithms for training SVM with smoothing methods are discussed in this paper. By smoothing the objective function of an SVM formulation, Lee and Mangasarian [Comput Optim Appl 20(1):5-22, 2001] presented one such algorithm called SSVM and proved that the error bound between the new smooth problem and the original one was O(1/p) for large positive smoothing paramet...

In this paper, we combine the well-established technique of Wiener filtering with an efficient method for robust smoothing: channel smoothing. The main parameters to choose in channel smoothing are the number of channels and the averaging filter. Whereas the number of channels has a natural lower bound given by the noise level and should for the sake of speed be as small as possible, the averag...