In classification problems, isotonic regression has been commonly used to map the prediction scores to posterior class probabilities. However, isotonic regression may suffer from overfitting, and the learned mapping is often discontinuous. Besides, current efforts mainly focus on the calibration of a single classifier. As different classifiers have different strengths, a combination of them can...

rectangular waveguide (wr) is one of the earliest types of transmission lines. rectangular waveguide can be produced by hot extrusion process. in this paper, the hot extrusion process of cuzn5 rectangular waveguide was investigated by finite element method (fem). in addition, genetic algorithm method (ga) was used to optimize the die geometry and process conditions to achieve the lowest magnitu...

today, with rapid growth of the world wide web and creation of internet sites and online text resources, text summarization issue is highly attended by various researchers. extractive-based text summarization is an important summarization method which is included of selecting the top representative sentences from the input document. when, we are facing into large data volume documents, the extr...

Estimation (Forecasting) of industrial production costs is one of the most important factor affecting decisions in the highly competitive markets. Thus, accuracy of the estimation is highly desirable. Hibrid Regression Neural Network is an approach proposed in this paper to obtain better fitness in comparison with Regression Analysis and the Neural Network methods. Comparing the estimated resul...

Symbolic regression is an important but challenging research topic in data mining. It can detect the underlying mathematical models. Genetic programming (GP) is one of the most popular methods for symbolic regression. However, its convergence speed might be too slow for large scale problems with a large number of variables. This drawback has become a bottleneck in practical applications. In thi...

Stepwise regression includes regression models in which the predictive variables are selected by an automated algorithm. The stepwise method involves two approaches, namely, backward elimination and forward selection. Currently, SAS has several regression procedures capable of performing stepwise regression. Among them are REG, LOGISTIC, GLMSELECT and PHREG. PROC REG handles linear regression m...

This paper proposes an example-based superresolution algorithm for multi-spectral remote sensing images. The underlying idea of this algorithm is to learn a matrix-based implicit prior from a set of high-resolution training examples to model the relation between LR and HR images. The matrixbased implicit prior is learned as a regression operator using conjugate decent method. The direct relatio...