It is a common practice to model an object for detection tasks as a boosted ensemble of many models built on features of the object. In this context, features are defined as subre...
We present an empirical comparison of the AUC performance of seven supervised learning methods: SVMs, neural nets, decision trees, k-nearest neighbor, bagged trees, boosted trees,...
In this study, a multistage neural network ensemble learning model is proposed to evaluate credit risk at the measurement level. The proposed model consists of six stages. In the ...
In this paper, we present two ensemble learning algorithms which make use of boostrapping and out-of-bag estimation in an attempt to inherit the robustness of bagging to overfitti...
In this study we propose a new ensemble model composed of several linear perceptrons. The objective of this study is to build a piecewise-linear classifier that is not only compet...