Abstract: Ensemble learning, which combines multiple models, has performed well in many tasks. Unsupervised ensemble classification is the focus here. Unsupervised means the ensemble combiner does not know the ground-truth labels each classifier was trained…
Abstract: Security applications employ machine learning models. However, attackers can adapt to avoid detection. Machine learning models have been strengthened against such attacks. However, randomization is underutilized. Most studies use models with differentiable error functions,…
Abstract: This paper introduces an unsupervised feature learning architecture based on a multi-clustering integration module and MIRBM, a variant of RBM. The multi-clustering integration module uses three clusterers—K-means, affinity propagation, and spectral—to generate three clustering…
Abstract: Unsupervised domain adaptation is effective in leveraging rich information from a labeled source domain to an unlabeled target domain. Though deep learning and adversarial strategy made a significant breakthrough in the adaptability of features,…
Abstract: In this paper, we study an automatic hypothesis generation (HG) problem, which involves finding meaningful implicit connections between scientific terms like diseases, chemicals, drugs, and genes from biomedical publication databases. Most previous studies used…
Abstract: To serve a user, an intelligent robot needs episodic memory to recall a sequence of events from past experiences. ART networks have been used to design episodic memories that incrementally learn new tasks without…
Abstract: Detecting small low-contrast airspace targets is crucial and difficult. This article proposes a simple and effective data-driven support vector machine (SVM)-based spatiotemporal feature fusion detection method for small low-contrast targets. A spatiotemporal profile, a…
Abstract: Human concept learning is semi-supervised learning (SSL) because people naturally combine a few labeled data with lots of unlabeled data to make classification decisions. Human concept learning is both static and dynamic. To accommodate…
Abstract: Graph-based clustering partitions data by similarity graph and performs well on many tasks. When data has noise and outliers, it’s hard to make a good similarity graph, which determines clustering results. This article proposes…
Abstract: Time-driven learning updates prediction model parameters as new data arrives in this work. The direct heuristic dynamic programming (dHDP) algorithm has been shown to solve several complex learning control problems. As system states change,…
Abstract: Online autonomous learning algorithms mine data streams with minimal computational complexity. Due to their structural simplicity, parsimonious learning machines (PALMs) are suitable for such applications. These parsimonious algorithms use thresholds to add or remove…
Abstract: Machine learning’s multi-scale decision system (MDS) describes hierarchical data well. MDS knowledge discovery involves OSC selection and attribute reduction. Searching for all OSCs may cause a combinatorial explosion, and current methods are time-consuming. This…
WhatsApp us