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Found 9 papers, 2 papers with code
LiDAR-Guided Cross-Attention Fusion for Hyperspectral Band Selection and Image Classification
The fusion of hyperspectral and LiDAR data has been an active research topic.
Knowledge Graphs and Pre-trained Language Models enhanced Representation Learning for Conversational Recommender Systems
In our KERL framework, entity textual descriptions are encoded via a pre-trained language model, while a knowledge graph helps reinforce the representation of these entities.
Towards Data-centric Graph Machine Learning: Review and Outlook
Data-centric AI, with its primary focus on the collection, management, and utilization of data to drive AI models and applications, has attracted increasing attention in recent years.
A Survey of Machine Unlearning
Specifically, as a category collection of cutting-edge studies, the intention behind this article is to serve as a comprehensive resource for researchers and practitioners seeking an introduction to machine unlearning and its formulations, design criteria, removal requests, algorithms, and applications.
Ensemble Learning based on Classifier Prediction Confidence and Comprehensive Learning Particle Swarm Optimisation for polyp localisation
Based on an observation that different segmentation algorithms will perform well on different subsets of examples because of the nature and size of training sets they have been exposed to and because of method-intrinsic factors, we propose to measure the confidence in the prediction of each algorithm and then use an associate threshold to determine whether the confidence is acceptable or not.
Streaming Active Deep Forest for Evolving Data Stream Classification
In recent years, Deep Neural Networks (DNNs) have gained progressive momentum in many areas of machine learning.
Conditional Random Field and Deep Feature Learning for Hyperspectral Image Segmentation
Image segmentation is considered to be one of the critical tasks in hyperspectral remote sensing image processing.
An ensemble-based online learning algorithm for streaming data
In this study, we introduce an ensemble-based approach for online machine learning.
Aggregation of Classifiers: A Justifiable Information Granularity Approach
In this study, we introduce a new approach to combine multi-classifiers in an ensemble system.
