Gaussian Processes (GPs) have state of the art performance in regression. In GPs, all the basis functions are required for prediction; hence its test speed is slower than other learning algorithms such as support vector machines (SVMs), relevance vector machine (RVM), adaptive sparseness (AS), etc. To overcome this limitation, we present a backward elimination algorithm, called GPs-BE that recu...

Variable selection and transformation selection are two commonly encountered problems in the linear model. It is often of interest to combine these two procedures in an analysis. Due to recent developments in computing technology, such a procedure is now feasible. In this paper, we propose two variable and transformation selection procedures on the predictor variables in the linear model. The r...

gression enable us to investigate the relationship between a categorical outcome and a set of explanatory variables. The outcome or response can be either dichotomous (yes, no) or ordinal (low, medium, high). During dichotomous response, we are performing standard logistic regression and for ordinal response, model that uses standard logistic regression formula with feature selection using forw...

CLASSIFICATION RICE QUALITY USING K-NN BACKWARD-BASED ELIMINATIONRice is one of the most important agricultural products. And it a strategic commodity because almost all Indonesian people need it. Because importance function rice as staple food ingredient, quality to be consumed must ensured high quality. Determination or until now has been done by many previous researchers. However, several me...

The inverse method is a generic proof search procedure applicable to non-classical logics satisfying cut elimination and the subformula property. In this paper we describe a general architecture and several high-level optimizations that enable its efficient implementation. Some of these rely on logic-specific properties, such as polarization and focusing, which have been shown to hold in a wide...

This paper describes an automated model selection method for analysing the relationship between the order of the spherical harmonic basis functions used to fit high angular resolution diffusion imaging (HARDI) data and the accuracy of the fitting results. The method performs statistical inference on the spherical harmonic expansion coefficients and uses a backward elimination procedure to remov...

Variable selection serves a dual purpose in statistical classification problems: it enables one to identify the input variables which separate the groups well, and a classification rule based on these variables frequently has a lower error rate than the rule based on all the input variables. Kernel Fisher discriminant analysis (KFDA) is a recently proposed powerful classification procedure, fre...

Consider the linear system of equations of the form Ax = b where the coefficient matrix A ∈ Rn×n is nonsingular, large, sparse and nonsymmetric and also x, b ∈ R. We refer to this system as the original system. An explicit preconditioner M for this system is an approximation of matrix A−1. In [1], Lou presented the Backward Factored INVerse or BFINV algorithm which computes the inverse factoriz...

We present and study the contribution-selection algorithm (CSA), a novel algorithm for feature selection. The algorithm is based on the multiperturbation shapley analysis (MSA), a framework that relies on game theory to estimate usefulness. The algorithm iteratively estimates the usefulness of features and selects them accordingly, using either forward selection or backward elimination. It can ...