Suchergebnisse - Clustering—Algorithms Keywords Active semi-supervised learning~

MAEL: meta-active semi-supervised ensemble learning model for DDoS attack detection.

Autoren: Saidane, Ahmed El Kamel, Ali Youssef, Habib

Quelle: Cluster Computing; Nov2025, Vol. 28 Issue 12, p1-15, 15p

Schlagwörter: DENIAL of service attacks, ENSEMBLE learning, SUPERVISED learning, ANOMALY detection (Computer security), ACTIVE learning, MACHINE learning, CLUSTERING algorithms, COMPUTER performance

FA-Seed: Flexible and Active Learning-Based Seed Selection.

Autoren: Vu, Dinh Minh Nguyen, Thanh Son

Quelle: Information; Oct2025, Vol. 16 Issue 10, p884, 21p

Schlagwörter: ACTIVE learning, CLUSTERING algorithms, FUZZY clustering technique, SUPERVISED learning

CLAIRE: A Four-Layer Active Learning Framework for Enhanced IoT Intrusion Detection.

Autoren: Almalawi, Abdulmohsen

Quelle: Electronics (2079-9292); Nov2025, Vol. 14 Issue 22, p4547, 26p

Schlagwörter: INTERNET of things, INTRUSION detection systems (Computer security), INTERNET security, ACTIVE learning, UNCERTAINTY (Information theory), CLUSTERING algorithms, DATA structures, MACHINE learning

OpenLabCluster: active learning based clustering and classification of animal behaviors based on kinematic body keypoints.

Autoren: Li, Jingyuan Keselman, Moishe Shlizerman, Eli

Quelle: Frontiers in Systems Neuroscience; 2025, p1-13, 13p

Schlagwörter: ACTIVE learning, ANIMAL behavior, CLASSIFICATION, BEHAVIORAL assessment, POSE estimation (Computer vision), CLUSTERING algorithms

Active semi-supervised learning using sampling theory for graph signals

Autoren: Akshay Gadde Aamir Anis Antonio Ortega et al.

Weitere Verfasser: Akshay Gadde Aamir Anis Antonio Ortega et al.

Quelle: http://arxiv.org/pdf/1405.4324.pdf.

Schlagwörter: Categories and Subject Descriptors I.5.2 [Pattern Recognition, Design Methodology—Clas- sifier design and evaluation, I.5.3 [Pattern Recognition, Clustering—Algorithms Keywords Active semi-supervised learning, Graph signal processing, Sampling theory, Graph signal filtering

Dateibeschreibung: application/pdf

Relation: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.746.8387; http://arxiv.org/pdf/1405.4324.pdf

Verfügbarkeit: http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.746.8387
http://arxiv.org/pdf/1405.4324.pdf

基于水波传播特性的半监督密度聚类算法. (Chinese)

Alternate Title: Semi-supervised density clustering algorithm based on water wave propagation characteristics. (English)

Autoren: 冯彦超 王 杰 栗雅婷 et al.

Quelle: Application Research of Computers / Jisuanji Yingyong Yanjiu; Aug2025, Vol. 42 Issue 8, p2329-2334, 6p

Schlagwörter: WATER waves, CLUSTERING algorithms, MASS density gradients, SUPERVISED learning, APPLIED sciences

Semi-supervised batch active learning based on mutual information: Semi-supervised batch active learning based on mutual information: X. Ji et al.

Autoren: Ji, Xia Wang, LingZhu Fang, XiaoHao

Quelle: Applied Intelligence; Jan2025, Vol. 55 Issue 2, p1-18, 18p

Semi-Supervised Soft Computing for Ammonia Nitrogen Using a Self-Constructing Fuzzy Neural Network with an Active Learning Mechanism.

Autoren: Zhou, Hongbiao Huang, Yang Yang, Dan et al.

Quelle: Water (20734441); Oct2024, Vol. 16 Issue 20, p3001, 26p

Schlagwörter: FUZZY neural networks, STANDARD deviations, SOFT computing, K-means clustering, WATER quality

Semi-Supervised Clustering Algorithms Through Active Constraints.

Autoren: Almazroi, Abdulwahab Ali Atwa, Walid

Quelle: International Journal of Advanced Computer Science & Applications; Jul2024, Vol. 15 Issue 7, p338-345, 8p

Ranking-enhanced anomaly detection using Active Learning-assisted Attention Adversarial Dual AutoEncoder.

Autoren: Benabderrahmane, Sidahmed Cheney, James Rahwan, Talal

Quelle: Scientific Reports; 11/24/2025, Vol. 15 Issue 1, p1-42, 42p

Schlagwörter: ANOMALY detection (Computer security), ACTIVE learning, AUTOENCODERS, INFORMATION asymmetry, INTERNET security

Active learning for industrial applications.

Autoren: Cacciarelli, Davide Kulahci, Murat

Quelle: Quality Engineering; 2025, Vol. 37 Issue 2, p175-184, 10p

Schlagwörter: ACTIVE learning, INDUSTRIAL applications, REGRESSION analysis, CLASSIFICATION algorithms, INSPECTION & review, CLASSIFICATION, SAMPLING methods

Active Semi-Supervised Clustering Algorithm for Multi-Density Datasets.

Autoren: Atwa, Walid Almazroi, Abdulwahab Ali Aldhahr, Eman A. et al.

Quelle: International Journal of Advanced Computer Science & Applications; Oct2024, Vol. 15 Issue 10, p493-500, 8p

A Real-Time Semi-Supervised Log Anomaly Detection Framework for ALICE O 2 Facilities.

Autoren: Techaviseschai, Arnatchai Tarnpradab, Sansiri Barroso, Vasco Chibante et al.

Quelle: Applied Sciences (2076-3417); Jun2025, Vol. 15 Issue 11, p5901, 27p

Schlagwörter: MACHINE learning, CLUSTERING algorithms, LARGE Hadron Collider, COMPUTER systems, NUCLEAR research

Semi-Supervised Fuzzy Clustering Based on Prior Membership.

Autoren: Hong, Yinghan Zhong, Guoxiang Lian, Jiahao et al.

Quelle: Mathematics (2227-7390); Aug2025, Vol. 13 Issue 16, p2559, 18p

Schlagwörter: FUZZY clustering technique, ENTROPY (Information theory), SUPERVISED learning, CLUSTER analysis (Statistics)

LLM-Driven Active Learning for Dependency Analysis of Mobile App Requirements Through Contextual Reasoning and Structural Relationships.

Autoren: Almoqren, Nuha Alrashoud, Mubarak

Quelle: Applied Sciences (2076-3417); Sep2025, Vol. 15 Issue 18, p9891, 29p

Schlagwörter: ACTIVE learning, CONTEXTUAL analysis, STATISTICAL correlation, CUSTOMER feedback, HYPOTHESIS, LANGUAGE models, MOBILE apps, NATURAL language processing

A review on multi-view learning.

Autoren: Yu, Zhiwen Dong, Ziyang Yu, Chenchen et al.

Quelle: Frontiers of Computer Science; Jul2025, Vol. 19 Issue 7, p1-19, 19p

Website Owner Identification through Multi-level Contrastive Representation Learning.

Autoren: Tu, Cheng Ma, Yunshan Li, Yang et al.

Quelle: ACM Transactions on Knowledge Discovery from Data; Nov2025, Vol. 19 Issue 9, p1-39, 39p

Schlagwörter: LANGUAGE models, MACHINE learning, CLUSTERING algorithms, INFORMATION retrieval, DATA mining

Characterizing and clustering debris flow and environmental noise seismic signals using unsupervised deep learning.

Autoren: Huang, Zhiyong Yang, Zongji Pang, Bo et al.

Quelle: Geophysical Journal International; Nov2025, Vol. 243 Issue 2, p1-18, 18p

Schlagwörter: SIGNAL classification, DEEP learning, DEBRIS avalanches, REAL-time computing, ACOUSTIC emission, SPECTROGRAMS, CLUSTERING algorithms

Mathematical Optimization in Machine Learning for Computational Chemistry.

Autoren: Zekić, Ana

Quelle: Computation; Jul2025, Vol. 13 Issue 7, p169, 23p

Schlagwörter: MATHEMATICAL optimization, MACHINE learning, COMPUTATIONAL chemistry, SUBGRADIENT methods, SURROGATE-based optimization, ACTIVE learning, MOLECULAR dynamics

Clustering Algorithms and RAG Enhancing Semi-Supervised Text Classification with Large LLMs.

Autoren: Zhong, Shan Zeng, Jiahao Yu, Yongxin et al.

Quelle: International Journal of Data Science & Analytics; Nov2025, Vol. 20 Issue 6, p5377-5398, 22p