Search
Search Results
-
Spike and slab Bayesian sparse principal component analysis
Sparse principal component analysis (SPCA) is a popular tool for dimensionality reduction in high-dimensional data. However, there is still a lack of...
-
Bi-objective evolutionary Bayesian network structure learning via skeleton constraint
Bayesian network is a popular approach to uncertainty knowledge representation and reasoning. Structure learning is the first step to learn a...
-
Auto focusing of in-Line Holography based on Stacked Auto Encoder with Sparse Bayesian Regression and Compressive Sensing
In recent years, Digital holography has emerged as an exceptional imaging technology for tracking high-contrast object particles and, interestingly,...
-
Constraining acyclicity of differentiable Bayesian structure learning with topological ordering
Distributional estimates in Bayesian approaches in structure learning have advantages compared to the ones performing point estimates when handling...
-
PAC-Bayesian offline Meta-reinforcement learning
Meta-reinforcement learning (Meta-RL) utilizes shared structure among tasks to enable rapid adaptation to new tasks with only a little experience....
-
Root Sparse Bayesian Learning-Based 2-D Off-Grid DOA Estimation Algorithm for Massive MIMO Systems
Traditional sparse Bayesian learning (SBL)-based two-dimensional (2-D) direction of arrival (DOA) estimation algorithms exhibit limited accuracy in...
-
A Novel Multiple Feature-Based Engine Knock Detection System using Sparse Bayesian Extreme Learning Machine
Automotive engine knock is an abnormal combustion phenomenon that affects engine performance and lifetime expectancy, but it is difficult to detect....
-
An efficient evolutionary algorithm based on deep reinforcement learning for large-scale sparse multiobjective optimization
Large-scale sparse multiobjective optimization problems (SMOPs) widely exist in academic research and engineering applications. The curse of...
-
Efficient Bayesian Learning of Sparse Deep Artificial Neural Networks
In supervised Machine Learning (ML), Artificial Neural Networks (ANN) are commonly utilized to analyze signals or images for a variety of...
-
Distributed sparse learning for stochastic configuration networks via alternating direction method of multipliers
As a class of randomized learning algorithms, stochastic configuration networks (SCNs) have demonstrated excellent capabilities in various...
-
Vibration-Based Structural Damage Detection Using Sparse Bayesian Learning Techniques
Vibration-based structural damage detection constantly involves uncertainties, including measurement noise, methodology, and modeling errors....
-
Variational Bayesian multi-sparse component extraction for damage reconstruction of space debris hypervelocity impact
To improve the survivability of orbiting spacecraft against space debris impacts, we propose an impact damage assessment method. First, a multi-area...
-
Ensemble learning based anomaly detection for IoT cybersecurity via Bayesian hyperparameters sensitivity analysis
The Internet of Things (IoT) integrates more than billions of intelligent devices over the globe with the capability of communicating with other...
-
A Bayesian reinforcement learning approach in markov games for computing near-optimal policies
Bayesian Learning is an inference method designed to tackle exploration-exploitation trade-off as a function of the uncertainty of a given...
-
A survey of Bayesian Network structure learning
Bayesian Networks (BNs) have become increasingly popular over the last few decades as a tool for reasoning under uncertainty in fields as diverse as...
-
Learning from crowds with sparse and imbalanced annotations
Traditional supervised learning requires ground truth labels for training, whose collection however is difficult in many cases. Recently,...
-
A fast weighted multi-view Bayesian learning scheme with deep learning for text-based image retrieval from unlabeled galleries
In this paper, we propose a new computationally fast method for text-based image retrieval from unlabeled galleries, where retrieval is formulated as...
-
Sparse and Outlier Robust Extreme Learning Machine Based on the Alternating Direction Method of Multipliers
Extreme learning machine (ELM) has been extensively researched for its fast training speed and powerful learning abilities. Entering the era of big...
-
BRL-ETDM: Bayesian reinforcement learning-based explainable threat detection model for industry 5.0 network
To enhance the universal adaptability of the Real-Time deployment of Industry 5.0, various machine learning-based cyber threat detection models are...
