Deep neural networks have enhanced the performance of decision making systems in many
applications, including image understanding, and further gains can be achieved by …

N-ary error correcting output codes (ECOC) decompose a multi-class problem into simpler
multi-class problems by splitting the classes into N subsets (meta-classes) to form an …

Ensemble learning, online learning and deep learning are very effective and versatile in a
wide spectrum of problem domains, such as feature extraction, multi-class classification and …

Convolutional autoencoders (CAEs) have shown their remarkable performance in stacking
to deep convolutional neural networks (CNNs) for classifying image data during the past …

Ensembling is a popular and powerful technique to utilize predictions from several different
machine learning models. The fundamental precondition of a well-working ensemble model …

Ensemble-based systems are well known to have the capacity to outperform individual
approaches if the ensemble members are sufficiently accurate and diverse. This paper …

In this paper, we propose a new heuristic training procedure to help a deep neural network
(DNN) repeatedly escape from a local minimum and move to a better local minimum. Our …

In computer vision and image analysis, Convolutional Neural Networks (CNNs) and other
deep-learning models are at the forefront of research and development. These advanced …

Ensemble methods for classification problems construct a set of models, often called"
learners", and then assign class labels to new data points by taking a combination of the …

Artificial neural networks have been successfully applied to a variety of machine learning
tasks, including image recognition, semantic segmentation, and machine translation …