We propose a combined fusion-selection approach to classifier ensemble design. Each
classifier in the ensemble is replaced by a miniensemble of a pair of subclassifiers with a …

Obtaining detailed information about the amount of forest cover is an important issue for
governmental policy and forest management. This paper presents a new approach to …

This paper introduces Learn++, an ensemble of classifiers based algorithm originally
developed for incremental learning, and now adapted for information/data fusion …

Boosting is used to determine the order in which classifiers are aggregated in a bagging
ensemble. Early stopping in the aggregation of the classifiers in the ordered bagging …

The main goal of this paper is to investigate the relationship between two theories widely
applied to explain the success of classifiers fusion: diversity measures and margin theory. In …

In this paper, the application of multiple Elman neural networks to time series data
regression problems is studied. An ensemble of Elman networks is formed by boosting to …

集成学习可以提高分类器的泛化性能, 这种方法已经成为机器学习的重要研究方向之一. 通常,
集成学习主要由2 部分构成, 即个体生成方法及结论生成方法. 从集成学习的差异性角度出发 …

In practical biometric verification applications, we expect to observe a large variability of
biometric data. Single classifiers have insufficient accuracy in such cases. Fusion of multiple …

In this paper, we generalize the problem of multi-classifiers combination by using modified
bagging method to detect previously unknown viruses. The detection engine applies two …

There is an increasing number of multimedia collections arising in areas once only the
domain of text and 2-D images. Richer types of multimedia such as audio, video and 3-D …