Suchergebnisse - "Machine learning Research."

Machine learning approaches to injury risk prediction in sport: a scoping review with evidence synthesis

Autoren: Christopher Leckey Nicol van Dyk Cailbhe Doherty et al.

Quelle: Br J Sports Med

Schlagwörter: SDG-03: Good health and well-being, Machine learning research, SDG-09: Industry, Injury risk prediction, Risk Assessment, Machine Learning, 03 medical and health sciences, Logistic Models, 0302 clinical medicine, Sports-related injuries, Area Under Curve, Athletic Injuries, Humans, Systematic Review, Sports, innovation and infrastructure

Dateibeschreibung: application/pdf

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39613453

Efficient annotation reduction with active learning for computer vision-based Retail Product Recognition

Autoren: Griffioen, Niels Rankovic, Nevena Zamberlan, Federico et al.

Quelle: Journal of computational social science

Schlagwörter: Artificial intelligence, Active learning, 4609 Information systems, Semi-Supervised Learning, Annotation, Social Sciences, Active Learning, 02 engineering and technology, International trade, Fabric Defect Detection in Industrial Applications, Quantum mechanics, 7. Clean energy, Annotation effort, Industrial and Manufacturing Engineering, Learning with Noisy Labels in Machine Learning, 12. Responsible consumption, Engineering, Deep Learning, Sociology, Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Entropy (arrow of time), Business, Retail image analysis, Active Learning in Machine Learning Research, 4701 Communication and media studies, Physics, 4. Education, Retraining, 4410 Sociology, Social Sciences, Mathematical Methods, Active learning (machine learning), 15. Life on land, Computer science, Algorithm, Computer Science, Physical Sciences, Instance segmentation, Mathematics, Mathematical Methods In Social Sciences, Robust Learning

Zugangs-URL: https://research.tilburguniversity.edu/en/publications/544d20d5-2b8e-4483-b9dd-b2be4d161c17
https://doi.org/10.1007/s42001-024-00266-7
https://lirias.kuleuven.be/handle/20.500.12942/744482
https://doi.org/10.1007/s42001-024-00266-7
https://repository.uantwerpen.be/docstore/d:irua:23253
https://hdl.handle.net/10067/2054360151162165141

Machine Learning and Industrial Strategy of Resilient and Predictive Maintenance: Proposal of A Conceptual Research Model in Morocco

Autoren: LAKHLIFI Fatima-Zahrae IDRISSI Adil ABDELLAOUI Mohammed et al.

Quelle: International Journal of Social Science and Human Research.

Schlagwörter: Machine learning, Research proposal, Resilience, Industry, Morocco

Universal consistency of the k-NN rule in metric spaces and Nagata dimension. II

Autoren: Sushma Kumari Vladimir Pestov

Quelle: ESAIM: Probability and Statistics. 28:132-160

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Metric (unit), Learning and Inference in Bayesian Networks, Economics, Separable space, Space (punctuation), Mathematical analysis, Metric Spaces, Machine Learning (cs.LG), Computational Complexity and Algorithmic Information Theory, Artificial Intelligence, Euclidean space, FOS: Mathematics, Active Learning in Machine Learning Research, Pure mathematics, Discrete mathematics, Minkowski–Bouligand dimension, Computer science, Packing dimension, Operating system, Dimension (graph theory), Operations management, Computational Theory and Mathematics, Computer Science, Physical Sciences, Metric space, Fractal dimension, Fractal, 62H30, 54F45, Mathematics, Consistency (knowledge bases)

Zugangs-URL: http://arxiv.org/abs/2305.17282

Language Outlay for the Sokoto Cement Production System Using Deterministic Finite Automata Scheme

Autoren: Zaid Ibrahim

Quelle: European Journal of Theoretical and Applied Sciences. 1:105-114

Schlagwörter: Algebraic number, Automaton, Economics, Automata Theory and Formal Languages, Macroeconomics, 0211 other engineering and technologies, 02 engineering and technology, Mathematical analysis, 12. Responsible consumption, Theoretical computer science, Artificial Intelligence, 11. Sustainability, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Production (economics), Active Learning in Machine Learning Research, Scheme (mathematics), Algebra over a field, Arithmetic, Text Compression and Indexing Algorithms, 4. Education, Formal Languages, Pure mathematics, Semantics (computer science), Discrete mathematics, 15. Life on land, Substitution (logic), Computer science, Process (computing), Programming language, Computational Theory and Mathematics, 13. Climate action, Computer Science, Physical Sciences, 8. Economic growth, Binary number, Mathematics

Simplicity Bias in Transformers and their Ability to Learn Sparse Boolean Functions

Autoren: Bhattamishra, S Patel, A Kanade, V et al.

Quelle: Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). :5767-5791

Schlagwörter: Artificial neural network, FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Economics, Semi-Supervised Learning, Generalization, Overfitting, 02 engineering and technology, Mathematical analysis, 01 natural sciences, Machine Learning (cs.LG), Task (project management), Engineering, Artificial Intelligence, Boolean function, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Natural Language Processing, 0105 earth and related environmental sciences, Transformer, Computer Science - Computation and Language, Inductive bias, Text Compression and Indexing Algorithms, Voltage, Computer science, Management, Algorithm, Multi-task learning, Electrical engineering, Computer Science, Physical Sciences, Computation and Language (cs.CL), Mathematics

Zugangs-URL: http://arxiv.org/abs/2211.12316
https://ora.ox.ac.uk/objects/uuid:9c4af441-8435-4767-b0eb-dec15bce2ce5
https://doi.org/10.18653/v1/2023.acl-long.317

Fast and Informative Model Selection Using Learning Curve Cross-Validation

Autoren: Felix Mohr Jan N. van Rijn

Quelle: IEEE Transactions on Pattern Analysis and Machine Intelligence. 45:9669-9680

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Semi-Supervised Learning, Handling Imbalanced Data in Classification Problems, Active Learning, 02 engineering and technology, Model selection, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Context (archaeology), Selection (genetic algorithm), Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Data mining, Biology, Cost-Sensitive Learning, Paleontology, Cross-validation, Computer science, Process (computing), Operating system, Computer Science, Physical Sciences, Algorithms, Learning Curve, Robust Learning, Labeled data, Training set

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/37028368
http://arxiv.org/abs/2111.13914

Statistical Analysis of Fixed Mini-Batch Gradient Descent Estimator

Autoren: Haobo Qi Feifei Wang Hansheng Wang

Quelle: Journal of Computational and Graphical Statistics. 32:1348-1360

Schlagwörter: FOS: Computer and information sciences, Artificial neural network, Artificial intelligence, Economics, Computational Mechanics, Sample size determination, Statistics - Computation, Estimator, Mathematical analysis, Engineering, Artificial Intelligence, FOS: Mathematics, Optimization Methods in Machine Learning, Large-Scale Optimization, Active Learning in Machine Learning Research, Computation (stat.CO), Economic growth, Stochastic Gradient Descent, Computer network, Gradient descent, Mathematical optimization, Statistics, Fixed point, Rate of convergence, Theory and Applications of Compressed Sensing, Applied mathematics, Computer science, Algorithm, Channel (broadcasting), Computer Science, Physical Sciences, Computation, Convergence (economics), Mathematics

Zugangs-URL: http://arxiv.org/abs/2304.06564

Beyond Autonomy: Large Language Models, Overconfidence, and the Irreducible Probability of Human Necessity

Autoren: Katz, Erickson orcid:0009-0008-1993-

Schlagwörter: Artificial intelligence, Machine learning, Human-Machine Interaction, Ethics in Artificial Intelligence, Computational Linguistics, Cognitive Science, Cognitive Science/education, Cognitive Science/methods, Cognitive Science/ethics, Human-in-the-Loop System, Machine Learning Research, AI Ethics

Relation: https://zenodo.org/records/17231346; oai:zenodo.org:17231346; https://doi.org/10.5281/zenodo.17231346

Verfügbarkeit: https://doi.org/10.5281/zenodo.17231346
https://zenodo.org/records/17231346

BaCO: A Fast and Portable Bayesian Compiler Optimization Framework

Autoren: Erik Orm Hellsten Artur Souza Johannes Lenfers et al.

Quelle: Proceedings of the 28th ACM International Conference on Architectural Support for Programming Languages and Operating Systems, Volume 4. :19-42

Schlagwörter: FOS: Computer and information sciences, Parallel computing, Computer Science - Machine Learning, Artificial intelligence, Hyperparameter Optimization, Compiler, 02 engineering and technology, Bayesian probability, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, Parallel Computing and Performance Optimization, GPU Computing, Computer architecture, Active Learning in Machine Learning Research, Bayesian optimization, Computer Science - Programming Languages, Computer Science - Performance, Performance Optimization, Computer science, Programming language, Performance (cs.PF), Hardware and Architecture, Optimizing compiler, Computer Science, Physical Sciences, Programming Languages (cs.PL)

Zugangs-URL: http://arxiv.org/abs/2212.11142

A theoretical distribution analysis of synthetic minority oversampling technique (SMOTE) for imbalanced learning

Autoren: Dina Elreedy Amir F. Atiya Firuz Kamalov

Quelle: Machine Learning. 113:4903-4923

Schlagwörter: Artificial intelligence, Class (philosophy), Conditional probability distribution, Semi-Supervised Learning, Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Pattern recognition (psychology), Mathematical analysis, Bandwidth (computing), Engineering, Artificial Intelligence, Machine learning, FOS: Electrical engineering, electronic engineering, information engineering, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Electrical and Electronic Engineering, Ensemble Methods, Active Learning in Machine Learning Research, SMOTE, Computer network, Imbalanced Data, Distribution (mathematics), Electricity Theft Detection in Smart Grids, Motion (physics), 4. Education, Oversampling, Statistics, Computer science, Algorithm, Computer Science, Physical Sciences, Interpolation (computer graphics), Mathematics

A machine learning-based image processing approach for robotic assembly system

Autoren: Wang, Xi Vincent, Dr. Soriano Pinter, Jaume Liu, Zhihao et al.

Quelle: Procedia CIRP. :906-911

Schlagwörter: assembly, image processing, machine learning, robot, Costs, Object recognition, Processing, Robot programming, Robotics, Advanced technology, Assembly process, Assembly systems, Image processing - methods, Images processing, Machine learning research, Object trajectories, Objects recognition, Processing approach, Robotic assembly

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317514
https://doi.org/10.1016/j.procir.2021.11.152

Semi-supervised learning with Bayesian Confidence Propagation Neural Network

Autoren: Ravichandran, Naresh Balaji Lansner, Anders, Professor Herman, Pawel

Quelle: ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning. :441-446

Schlagwörter: Backpropagation, Neural networks, Torsional stress, Bayesian, Cortexes, Internal representation, Learn+, Machine learning research, Neural network model, Neural-networks, Plausible model, Semi-supervised, Unlabeled data, Bayesian networks

Dateibeschreibung: print

Zugangs-URL: https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-317516
https://doi.org/10.14428/esann/2021.ES2021-156

Design Features and Methodological Quality of Researches about Prediction Models Based on Machine Learning in Primary Care: a Scoping Review

Autoren: ZHONG Jinjia, LI Wentao, HUANG Yafang, WU Hao

Quelle: Zhongguo quanke yixue, Vol 27, Iss 10, Pp 1271-1276 (2024)

Schlagwörter: Medicine, primary care|machine learning|research design|prediction model|methodological evaluation|scoping review

Zugangs-URL: https://doaj.org/article/23ec203a8839407fb16d0df4d252d889

Naive automated machine learning

Autoren: Felix Mohr Marcel Wever

Quelle: Machine Learning. 112:1131-1170

Schlagwörter: FOS: Computer and information sciences, Computer Science - Machine Learning, Artificial intelligence, Hyperparameter Optimization, Generalization, 02 engineering and technology, Mathematical analysis, Learning with Noisy Labels in Machine Learning, Machine Learning (cs.LG), Artificial Intelligence, Machine learning, FOS: Mathematics, 0202 electrical engineering, electronic engineering, information engineering, Swarm Intelligence Optimization Algorithms, Active Learning in Machine Learning Research, Constraint Handling, Global Optimization, Optimization Applications, Computer science, Programming language, Automated Machine Learning, Algorithm, Combinatorics, Computer Science, Physical Sciences, Tree (set theory), Pipeline (software), Mathematics

Zugangs-URL: http://arxiv.org/abs/2111.14514
https://epub.ub.uni-muenchen.de/107520/

Ingredients for Responsible Machine Learning: A Commented Review of The Hitchhiker’s Guide to Responsible Machine Learning

Autoren: Fernando Marmolejo‐Ramos Raydonal Ospina Enrique García-Ceja et al.

Weitere Verfasser: Fernando Marmolejo‐Ramos Raydonal Ospina Enrique García-Ceja et al.

Quelle: J Stat Theory Appl

Schlagwörter: 0301 basic medicine, Artificial intelligence, causality, Explainable Artificial Intelligence, Review, 01 natural sciences, Learning with Noisy Labels in Machine Learning, 03 medical and health sciences, Inference, Artificial Intelligence, Computer security, Machine learning, Interpretability, 0101 mathematics, Model Interpretability, Key (lock), Active Learning in Machine Learning Research, Visualization, inference, predictive statistics, Responsibility in AI, Computer science, Machine Learning Interpretability, Process (computing), Programming language, machine learning, Computer Science, Physical Sciences, Interpretable Models, Robust Learning, Workbench

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/36160758
https://hdl.handle.net/11541.2/31277

Classification in Multi-Label Datasets

Autoren: Aouatef, Mahani

Quelle: Information Systems Management ISBN: 9781803556512
Information Systems Management

Schlagwörter: FOS: Computer and information sciences, Artificial intelligence, Class (philosophy), Economics, Multi-label classification, Pattern recognition (psychology), Computer science, Detection and Prevention of Phishing Attacks, Management, Task (project management), Artificial Intelligence, Categorization, Multi-label Text Classification in Machine Learning, Computer Science, Physical Sciences, Machine learning, Text categorization, Multi-label Learning, Active Learning in Machine Learning Research, Data mining, Information Systems

Zugangs-URL: https://mts.intechopen.com/articles/show/title/classification-in-multi-label-datasets

Mitigating Machine Learning Bias Between High Income and Low-Middle Income Countries for Enhanced Model Fairness and Generalizability

Autoren: Jenny Yang Lei Clifton Nguyen Thanh Dung et al.

Quelle: Sci Rep
Scientific Reports, Vol 14, Iss 1, Pp 1-12 (2024)

Schlagwörter: Pooling, Artificial intelligence, Explainable Artificial Intelligence, Developing country, Economics, Developmental psychology, Semi-Supervised Learning, Low and middle income countries, FOS: Political science, Social Sciences, Machine Learning, Psychology, Equity (law), Business, Model Interpretability, 10. No inequality, Political science, Algorithmic Bias, 1. No poverty, FOS: Philosophy, ethics and religion, 3. Good health, FOS: Psychology, Vietnam, Physical Sciences, 8. Economic growth, Income, Medicine, Interpretable Models, Safety Research, Algorithms, Science, Generalizability theory, Health Informatics, Vietnamese, FOS: Law, Article, FOS: Economics and business, Bias, Artificial Intelligence, Capability Approach, Health Sciences, Machine learning, Humans, Econometrics, Developing Countries, Active Learning in Machine Learning Research, Economic growth, Artificial Intelligence in Medicine, SARS-CoV-2, Developed Countries, Ethical Implications of Artificial Intelligence, COVID-19, Linguistics, Computer science, United Kingdom, Philosophy, Computer Science, FOS: Languages and literature, Law

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/38858466
https://doaj.org/article/a88db8c825944c868781efc98703caa4

Quantifying the impact of uninformative features on the performance of supervised classification and dimensionality reduction algorithms

Autoren: Weihua Lei Cleber Zanchettin Zoey E. Ho et al.

Quelle: APL Machine Learning, Vol 1, Iss 4, Pp 046118-046118-10 (2023)

Schlagwörter: 0301 basic medicine, Artificial intelligence, QC1-999, Semi-Supervised Learning, Robustness (evolution), Handling Imbalanced Data in Classification Problems, 02 engineering and technology, Pattern recognition (psychology), Biochemistry, Gene, Learning with Noisy Labels in Machine Learning, 03 medical and health sciences, Artificial Intelligence, Meta-Learning, Machine learning, 0202 electrical engineering, electronic engineering, information engineering, Active Learning in Machine Learning Research, Data mining, Curse of dimensionality, Cost-Sensitive Learning, Imbalanced Data, Physics, 4. Education, QA75.5-76.95, 15. Life on land, Computer science, Dimensionality reduction, Algorithm, Chemistry, Electronic computers. Computer science, Computer Science, Physical Sciences, Feature selection, Robust Learning

Zugangs-URL: https://doaj.org/article/46de7007ca4e4133a547b5f336315b06

Embrace open-environment machine learning for robust AI

Autoren: Gang� Li Aswani Kumar Cherukuri

Quelle: Natl Sci Rev

Schlagwörter: Artificial intelligence, Active Learning, Transformative learning, 02 engineering and technology, Learning with Noisy Labels in Machine Learning, Artificial Intelligence, Machine learning, 11. Sustainability, 0202 electrical engineering, electronic engineering, information engineering, Psychology, Active Learning in Machine Learning Research, Adaptation to Concept Drift in Data Streams, Biology, Ecology, Special Topic: Machine Learning Automation, 9. Industry and infrastructure, Pedagogy, 4. Education, 15. Life on land, Computer science, Adaptability, FOS: Psychology, 13. Climate action, FOS: Biological sciences, Computer Science, Physical Sciences, Robust Learning

Zugangs-URL: https://pubmed.ncbi.nlm.nih.gov/39007001