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An extreme learning machine algorithm for semi-supervised classification of unbalanced data streams with concept drift
Data streams are important sources of information nowadays, and with the popularization of mobile devices and sensor systems that collect all kinds...
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A semi-supervised framework for concept-based hierarchical document clustering
Text clustering is used in various applications of text analysis. In the clustering process, the employed document representation method has a...
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Supervised contrastive learning with corrected labels for noisy label learning
Deep neural networks have achieved significant success in the artificial intelligence community and various downstream tasks. They encode images or...
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WikiCPRL: A Weakly Supervised Approach for Wikipedia Concept Prerequisite Relation Learning
Concept prerequisite relations determine the order in which knowledge concept is learned. This kind of concept relations has been used in a variety...
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Mitigating selection bias in counterfactual prediction through self-supervised domain embedding learning with virtual samples
Treatment effect estimation (TEE) is widely adopted in various domains such as machine learning, dvertising and marketing, and medicine. During the...
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Moderately supervised learning: definition, framework and generality
Learning with supervision has achieved remarkable success in numerous artificial intelligence (AI) applications. In the current literature, by...
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Single-Temporal Supervised Learning for Universal Remote Sensing Change Detection
Bitemporal supervised learning paradigm always dominates remote sensing change detection using numerous labeled bitemporal image pairs, especially...
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CISO: Co-iteration semi-supervised learning for visual object detection
Semi-supervised learning offers a solution to the high cost and limited availability of manually labeled samples in supervised learning. In...
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Series2vec: similarity-based self-supervised representation learning for time series classification
We argue that time series analysis is fundamentally different in nature to either vision or natural language processing with respect to the forms of...
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A Review of Predictive and Contrastive Self-supervised Learning for Medical Images
Over the last decade, supervised deep learning on manually annotated big data has been progressing significantly on computer vision tasks. But, the...
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Deep learning approaches for lyme disease detection: leveraging progressive resizing and self-supervised learning models
Lyme disease diagnosis poses a significant challenge, with blood tests exhibiting an alarming inaccuracy rate of nearly 60% in detecting early-stage...
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SCL-FExR: supervised contrastive learning approach for facial expression Recognition
Facial Expression Recognition (FER) is a significant field of computer vision and has emerged as a crucial component of Human-computer interaction....
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Interaction semantic segmentation network via progressive supervised learning
Semantic segmentation requires both low-level details and high-level semantics, without losing too much detail and ensuring the speed of inference....
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Supervised Learning
The fields of machine learning and artificial intelligence include the subfield of supervised learning, commonly known as supervised machine...
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A systematic review for class-imbalance in semi-supervised learning
This review aims to examine the state of the art of semi-supervised learning (SSL) techniques for addressing class imbalanced data. Class imbalance...
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Supervised contrastive learning with multi-scale interaction and integrity learning for salient object detection
Salient object detection (SOD) is designed to mimic human visual mechanisms to identify and segment the most salient part of an image. Although...
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SemiDocSeg: harnessing semi-supervised learning for document layout analysis
Document Layout Analysis (DLA) is the process of automatically identifying and categorizing the structural components (e.g. Text, Figure, Table,...
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Knowledge-aware reasoning with self-supervised reinforcement learning for explainable recommendation in MOOCs
Explainable recommendation is important but not yet explored in Massive Open Online Courses (MOOCs). Recently, knowledge graph (KG) has achieved...
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Novel strong supervised learning infusing expertisements: focused on warship classification model
Image detection deep learning models are being used in various fields such as autonomous vehicles, medical, and agriculture. In the defense field,...
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Weakly supervised pathological whole slide image classification based on contrastive learning
In the context of dealing with limited annotated data, this paper introduces a weakly supervised whole slide image (WSI) classification approach...
