This paper reports the error analysis results for the gridded daily precipitation data over the state of Texas of the United States from January 1, 1901 to December 31, 2000. The Global Daily Climatology Network dataset is used for both the data gridding and error analysis. The station data have been interpolated onto a 0.2° × 0.2° grid which starts at the base point (25°50′N, 106°38′W). The da...

To do that, randomly split the training data into ten equal-sized disjoint sets. For each value of the polynomial degree, d = 1, 2, 3, 4, plot the average cross-validation error plus or minus one standard deviation as a function of C (let the other parameters of polynomial kernels in libsvm, γ and c, be equal to their default values 1). Report the best value of the trade-off constant C measured...

We investigate the use of different Machine Learning methods to construct models for aqueous solubility. Models are based on about 4000 compounds, including an in-house set of 632 drug discovery molecules of Bayer Schering Pharma. For each method, we also consider an appropriate method to obtain error bars, in order to estimate the domain of applicability (DOA) for each model. Here, we investig...

speech emotion recognition (ser) is a new and challenging research area with a wide range of applications in man-machine interactions. the aim of a ser system is to recognize human emotion by analyzing the acoustics of speech sound. in this study, we propose spectral pattern features (sps) and harmonic energy features (hes) for emotion recognition. these features extracted from the spectrogram ...

Error density estimation in a nonparametric functional regression model with functional predictor and scalar response is considered. The unknown error density is approximated by a mixture of Gaussian densities with means being the individual residuals, and variance as a constant parameter. This proposed mixture error density has a form of a kernel density estimator of residuals, where the regre...

This paper reviews ve statistical tests for determining whether one learning algorithm out-performs another on a particular learning task. These tests are compared experimentally to determine their probability of incorrectly detecting a diierence when no diierence exists (type 1 error). Two widely-used statistical tests are shown to have high probability of Type I error in certain situations an...

Discrete Classification problems abound in pattern recognition and data mining applications. One of the most common discrete rules is the discrete histogram rule. This paper presents exact formulas for the computation of bias, variance, and RMS of the resubstitution and leave-one-out error estimators, for the discrete histogram rule. We also describe an algorithm to compute the exact probabilit...

Reliable estimation of the porous membranes neck-pore size distribution (NPSD) is the key element in the design and operation of all membrane separation processes. In this paper, a new approach is presented for reliable of NPSD of porous membranes using wet flow-state bubble point test data. For this purpose, a robust method based on the linear regularization theory is developed to extract NPSD...

ABSTRACT Estimation of mineral resources and reserves with low values of error is essential in mineral exploration. The aim of this study is to estimate and model a vein type deposit using disjunctive kriging method. Disjunctive Kriging (DK) as an appropriate nonlinear estimation method has been used for estimation of Cu values. For estimation of Cu values and modelling of the distributio...

5 L INTRODUCTION 6 2. ARTinCIAL NEURAL NETV/ORKS 8 2.L ANN Models 8 2.2. Introduction to Artificial Neural Networks 8 3. ERROR PREDICTION ON A GENERALIZER 13 3.1. Stacked Generalization 13 3.2. Error Prediction with Cross Validation Partition Criterion (CVPC) 14 3.3. Error Prediction with Bootstrap Partition Criterion (BPC) 17 3.4. Error Prediction with Modified Bootstrap Partition Criterion (M...