Two alternative designs to the ensemble classifier proposed in [13] are studied in this report. First, the out-of-bag error estimation is replaced with crossvalidation. Second, we incorporate AdaBoost and modify the weights of the individual training samples as the training progresses. The final decision is formed as a weighted combination of individual predictions rather than through majority ...

MOTIVATION Microarray classification typically possesses two striking attributes: (1) classifier design and error estimation are based on remarkably small samples and (2) cross-validation error estimation is employed in the majority of the papers. Thus, it is necessary to have a quantifiable understanding of the behavior of cross-validation in the context of very small samples. RESULTS An ext...

In this paper, we analyze the properties of cross-validation from the perspective of the stability, that is, the difference between the training error and the error of the selected model applied to any other finite sample. In both the i.i.d. and non-i.i.d. cases, we derive the upper bounds of the one-round and average test error, referred to as the one-round/convoluted Rademacher-bounds, to qua...

MOTIVATION Ranking feature sets is a key issue for classification, for instance, phenotype classification based on gene expression. Since ranking is often based on error estimation, and error estimators suffer to differing degrees of imprecision in small-sample settings, it is important to choose a computationally feasible error estimator that yields good feature-set ranking. RESULTS This pap...

This paper begins with a general theory of error in cross-validation testing of algorithms for supervised learning from examples. It is assumed that the examples are described by attribute-value pairs, where the values are symbolic. Cross-validation requires a set of training examples and a set of testing examples. The value of the attribute that is to be predicted is known to the learner in th...

in this research, the spatial distribution of fe, cu, zn and mn on agricultural lands of golestan province were evaluated using different interpolation methods such as, kriging, inverse distance weighted, local polynomial, inverse multiquadric and radial basis function. thus, 505 soil samples were provided from fields during 2008 and micronutrients rates were measured for each sample. the perfo...

This chapter discusses important aspects of the validation and verification of neural network models including selection of appropriate error metrics, analysis of residual errors and resampling methodologies for validation under conditions of sparse data. Error metrics reviewed include mean absolute error, root mean squared error, percent good and absolute distance error. The importance of inte...

Considering the importance of validation of customers in the cross-dock and since this is one of the problems of implementing cross-dock system in Iran, this study attempted to extract customer validation criteria. The purpose of the research is to eliminate the distrust of distributors in receiving the funds of the sent items and the statistical sample of this research is the experts of the sy...

Several radial basis function based methods contain a free shape parameter which has  a crucial role in the accuracy of the methods. Performance evaluation of this parameter in different  functions with various data has always been a topic of study. In the present paper, we consider studying the methods which determine an optimal value for the shape parameter in interpolations of radial basis  ...

We give a permutation approach to validation (estimation of out-sample error). One typical use of validation is model selection. We establish the legitimacy of the proposed permutation complexity by proving a uniform bound on the out-sample error, similar to a VC-style bound. We extensively demonstrate this approach experimentally on synthetic data, standard data sets from the UCI-repository, a...