Kernel based learning has already found wide applications to solve several data mining problems. In this paper, we proposed an improved linear kernel with automatic smoothing parameter (Sp) selection compared to the classical approach. Experiment results using some classification related benchmark datasets reveal that the improved linear kernel performed better than some existing kernel techniq...

for instruction in the language and principles of chemical engineering, many consultations and much useful advice. Appreciation is also due to the referees, whose comments on an earlier version of the paper have been invaluable.

In this paper we take a new look at smoothing Newton methods for solving the nonlinear complementarity problem (NCP) and the box constrained variational inequalities (BVI). Instead of using an infinite sequence of smoothing approximation functions, we use a single smoothing approximation function and Robinson’s normal equation to reformulate NCP and BVI as an equivalent nonsmooth equation H(u, ...

In Continuous Speech Recognition (CSR) systems a Language Model (LM) is required to represent the syntactic constraints of the language. Then a smoothing technique needs to be applied to avoid null LM probabilities. Each smoothing technique leads to a different LM probability distribution. Test set perplexity is usually used to evaluate smoothing techniques but the relationship with acoustic mo...

Abstract: Two existing approaches to functional principal components analysis (FPCA) are due to Rice and Silverman (1991) and Silverman (1996), both based on maximizing variance but introducing penalization in different ways. In this article we propose an alternative approach to FPCA using penalized rank one approximation to the data matrix. Our contributions are four-fold: (1) by considering i...

Penalized splines have become an increasingly popular tool for nonparametric smoothing because of their use of low-rank spline bases, which makes computations tractable while maintaining accuracy as good as smoothing splines. This article extends penalized spline methodology by both modeling the variance function nonparametrically and using a spatially adaptive smoothing parameter. This combina...

This paper proposes a numerically simple routine for locally adaptive smoothing. The locally heterogeneous regression function is modelled as a penalized spline with a smoothly varying smoothing parameter modelled as another penalized spline. This is being formulated as hierarchical mixed model, with spline coefficients following a normal distribution, which by itself has a smooth structure ove...

Word alignment is the basis of statistical machine translation. GIZA++ is a popular tool for producing word alignments and translation models. It uses a set of parameters that affect the quality of word alignments and translation models. These parameters exist to overcome some problems such as overfitting. This paper addresses the problem of tuning GIZA++ parameter for better translation qualit...

The accuracy and interpretation of results obtained by Diffusion Tensor Imaging (DTI) are largely influenced by several experimental parameter settings. In Voxel-Based (VB) analysis images are smoothed, in order to improve their Signal to Noise Ratio (SNR) and to reduce the impact of normalization and artifacts. This is a critical step and care must be taken so that directional information and ...

Smoothing splines via the penalized least squares method provide versatile and effective nonparametric models for regression with Gaussian responses. The computation of smoothing splines is generally of the order O.n3/, n being the sample size, which severely limits its practical applicability. We study more scalable computation of smoothing spline regression via certain low dimensional approxi...