Ensemble learning is a prolific field in Machine Learning since it is based on the assumption
that combining the output of multiple models is better than using a single model, and it …

Ensemble learning is an intensively studied technique in machine learning and pattern
recognition. Recent work in computational biology has seen an increasing use of ensemble …

We formally study how ensemble of deep learning models can improve test accuracy, and
how the superior performance of ensemble can be distilled into a single model using …

Though deep neural networks have achieved significant progress on various tasks, often
enhanced by model ensemble, existing high-performance models can be vulnerable to …

Automating sleep staging is vital to scale up sleep assessment and diagnosis to serve
millions experiencing sleep deprivation and disorders and enable longitudinal sleep …

The development of the computer-aided detection system placed an important role in the
clinical analysis for making the decision about the human disease. Among the various …

Recently, increasing attention has been directed to the study of the emotional content of
speech signals, and hence, many systems have been proposed to identify the emotional …

In the last decade, ensemble learning has become a prolific discipline in pattern recognition,
based on the assumption that the combination of the output of several models obtains better …

In the last years, the application of artificial intelligence methods on credit risk assessment
has meant an improvement over classic methods. Small improvements in the systems about …

Decision trees have become one of the most powerful and popular approaches in
knowledge discovery and data mining; it is the science of exploring large and complex …