Kernel target alignmentSoft margin SVMOne-class SVMThe two-stage multiple kernel learning (MKL) algorithms gained the popularity due to their simplicity and modularity. In this paper, we focus on two recently proposed two-stage MKL algorithms: ALIGNF...
Disclosed are methods and structures of Multiple Kernel learning framed as a standard binary classification problem with additional constraints that ensure the positive definiteness of the learned kernel. Advantageously, the disclosed methods and structures permit the use of binary classification technologies ...
With the advent of kernel methods, automating the task of specifying a suitable kernel has become increasingly important. In this context, the Multiple Kernel Learning (MKL) problem of finding a combination of prespecified base kernels that is suitable for the task at hand has received significant...
In most cases, TSGAT+Q-learning outperforms CPLEX, OR-Tools and other learning-based algorithms. Moreover, the trained networks can also solve the problem with varying numbers of maintenance tasks, which implies that TSGAT+Q-learning has good generalization ability. Finally, the proposed method ...
and contrasted with existing meta-heuristic algorithms like Grey Wolf Optimization algorithm (GWO)-ACA-ATRUNet-AMDN36, Honey Badger Algorithm (HBA)-ACA-ATRUNet-AMDN37, JAYA-ACA-ATRUNet-AMDN38, and EOO-ACA-ATRUNet-AMDN39 algorithm for representing the accuracy of the developed deep learning-bas...
recently by Yang [10] is taken as the main benchmark, whereas the default ELM and one of its variants, namely, the kernel ELM (KELM) [25], are also considered to depict the potential difference between standard versions and an algorithmically improved version of a same machine learning ...
The similarity with this article is that both use feature selection algorithms to screen features and reduce the interference of useless features on the experiment. The difference is that this article detects all 10 types of attacks, and all comparison indicators are 99.99%, which is higher than ...
Most essential works on Relation Extraction in the last decade were based on machine learning algorithms using a large number of hand-crafted features. Mainly, the top system of the DDIExtraction shared task [24] was a linear SVM classifier using a hybrid kernel with features based on syntactic...
Segmentation-Based Classification Deep Learning Model Embedded with Explainable AI for COVID-19 Detection in Chest X-ray Scans 2022, Diagnostics Using Machine Learning via Deep Learning Algorithms to Diagnose the Lung Disease Based on Chest Imaging: A Survey 2021, International Journal of Interactive ...
A novel deep learning-based two-stage RUL prognostic approach is presented in this paper by using fast search and find of density peaks clustering (FSFDPC) and multi-dimensional deep neural network (MDDNN). In the first stage, health states of the rolling bearing are automatically perceived by...