Recently, incremental and on-line learning gained more attention especially in the context of
big data and learning from data streams, conflicting with the traditional assumption of …

The accuracies for many pattern recognition tasks have increased rapidly year by year,
achieving or even outperforming human performance. From the perspective of accuracy …

Quantum Machine Learning bridges the gap between abstract developments in quantum
computing and the applied research on machine learning. Paring down the complexity of the …

One of the most popular tools for mining data streams are decision trees. In this paper we
propose a new algorithm, which is based on the commonly known CART algorithm. The …

Support vector machines (SVM) have drawn wide attention for the last two decades due to
its extensive applications, so a vast body of work has developed optimization algorithms to …

Network intrusion detection problem is an ongoing challenging research area because of a
huge number of traffic volumes, extremely imbalanced data sets, multi-class of attacks …

Recent years have witnessed an incredibly increasing interest in the topic of incremental
learning. Unlike conventional machine learning situations, data flow targeted by incremental …

In this paper, we propose a robust method for semisupervised training of deep neural
networks for multi-label image classification. To this end, we use ramp loss, which is more …

In this article, a new self-learning parallel control method, which is based on adaptive
dynamic programming (ADP) technique, is developed for solving the optimal control …

Support vector machine (SVM), as a useful tool of classification, has been widely applied in
many fields. However, it may incur computationally infeasibility on very large sample …