… And a learning algorithm that suffers from the representational problem is said to have high
"… Hence, ensemble methods can reduce both the bias and the variance of learning algorithms…

… Specifically, ensemble learning firstly extracts a set of features with a variety of … , multiple
learning algorithms are utilized to produce weak predictive results. Finally, ensemble learning …

… To verify the validity and efficiency of ensemble learning,we compare the adaboost algorithm
with the simple learning algorithms, include LS(least square), Perceptron and Stumps in …

… algorithms for training ensemble models, and transforming … of ensemble learning, explain
how ensemble learning work and … approaches for building an ensemble model and combining …

… This paper presents a novel dynamic ensemble learning (DEL) algorithm for designing
ensemble of neural networks (NNs). DEL algorithm determines the size of ensemble, the number …

… For homogeneous ensembles, the individual learners are called base learners, and the
corresponding learning algorithms are called base learning algorithms. In contrast, a …

… pillars of the ensemble systems: diversity, training ensemble members, and combining
ensemble members. After an overview of commonly used ensemble-based algorithms, we then …

This note presents a chronological review of the literature on ensemble learning which has
accumulated over the past twenty years. The idea of ensemble learning is to employ multiple …

… ensemble classification in the bioinformatics field. Here, we briefly review three main categories
of ensemble learning algorithms… tree algorithms and ensemble learning algorithms are …

… research as it interacts better with other ensemble learning algorithms, possibly because
it includes all training cases, allowing the other algorithm access to all cases on every run. …