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• The Rise of Machine Learning
  crossref 2019-03-29T19:09:31Z 置信度 0.70
• LEARNING FLEXIBLE CONCEPTS
  crossref Ryszard S. Michalski 2014-06-29T17:19:46Z 置信度 0.70
• Personality trait prediction by machine learning using physiological data and driving behavior
  crossref Morgane Evin, Antonio Hidalgo-Munoz, Adolphe James Béquet, Fabien Moreau 等 2022-06-11T04:53:22Z 置信度 0.70
• Auto-sklearn: Efficient and Robust Automated Machine Learning
  crossref Matthias Feurer, Aaron Klein, Katharina Eggensperger, Jost Tobias Springenberg 等 2019-05-17T13:44:23Z 置信度 0.70
• Machine Learning‐Based Hydrofacies Classification: Effects of Noise and Regularization
  Abstract This study utilizes an unsupervised ML approach, the expectation‐maximization (EM) algorithm using Gaussian Mixture Models (GMM), to integrate near‐surface geophysics measurements for hydrofacies classification. We examined the impact of noise and noi…
  crossref Emmanuel Oladeji, Andrew Parsekian, Dario Grana 2024-08-01T18:12:00Z 置信度 0.70
• teeny-tiny, adj.
  crossref 2023-07-21T07:20:47Z 置信度 0.70
• Tiny Patients, Tiny Dressings
  crossref Elizabeth L. Sharpe 2012-11-02T10:44:05Z 置信度 0.70
• Review for "Improving molecular machine learning through adaptive subsampling with active learning"
  crossref 2024-09-27T00:09:23Z 置信度 0.70
• Unsupervised learning for cluster discovery
  Introduction The objective of cluster discovery is to subdivide a given set of training data, X ≡ (x 1 , x 2 ,…, x N }, into a number of (say K ) subgroups. Even with unknown class labels of the training vectors, useful information may be extracted from the tr…
  crossref 2014-07-15T06:09:58Z 置信度 0.70
• Review for "Study becomes insight: Ecological learning from machine learning"
  crossref 2021-09-10T07:27:33Z 置信度 0.70
• Learning Classifier Systems
  crossref 2010-12-29T17:28:18Z 置信度 0.70
• Convex Learning Problems
  crossref 2014-07-15T05:22:55Z 置信度 0.70
• Mistake-Bounded Learning
  crossref 2010-12-29T17:30:36Z 置信度 0.70
• Machine Learning Basics
  crossref Tao Qin 2020-11-13T11:04:31Z 置信度 0.70
• Machine Learning Paradigms: Introduction to Deep Learning-Based Technological Applications
  crossref George A. Tsihrintzis, Lakhmi C. Jain 2020-07-23T20:02:49Z 置信度 0.70
• Prediction of learning outcomes via clickstream data using machine learning
  crossref Kelvin Hongrui Ng 2021-07-08T15:44:47Z 置信度 0.70
• Review for "Study becomes insight: Ecological learning from machine learning"
  crossref 2021-09-10T07:27:33Z 置信度 0.70
• Figure 7: Overview of machine learning and deep learning techniques for CHD detection.
  crossref 2024-11-29T05:08:00Z 置信度 0.70
• Machine Tool Fault Diagnosis Based on Support Vector Machine
  crossref 2023-02-06T05:26:28Z 置信度 0.70
• A novel approach to tele-rehabilitation: Implementing a biofeedback system using machine learning algorithms
  crossref Ali Barzegar Khanghah, Geoff Fernie, Atena Roshan Fekr 2023-09-17T17:40:30Z 置信度 0.70
• On the Complexity of Function Learning
  crossref Peter Auer, Philip M. Long, Wolfgang Maass, Gerhard J. Woeginger 2003-04-04T16:57:10Z 置信度 0.70
• Estimating Carbon Pools in the European Shelf Sea Environment: Replacing Reanalysis by Model‐Informed Machine Learning?
  Abstract Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse or highly uncertain. An alternative can be provided by carbon reanalyses (whether assimilating proxy variables, such as chlorop…
  crossref Jozef Skákala 2026-06-08T09:01:50Z 置信度 0.70
• AI in Real Estate: Forecasting House Prices with Advanced Machine Learning Models
  crossref Jiashuo Cui, Zhitong Liu, Yinghan Ma 2025-07-01T22:52:50Z 置信度 0.70
• A Reply to Zito-Wolf's Book Review of Learning Search Control Knowledge: An Explanation-Based Approach
  crossref Steven Minton 2003-04-04T16:55:36Z 置信度 0.70
• Machine Learning and Deep Learning Empowered Tweet Sentiment Analysis
  crossref Nida Hafeez, Abdullah, Maryam Shabbir, Fatima Shabbir 等 2026-05-21T19:40:47Z 置信度 0.70
• Shallow Learning
  crossref 2021-11-23T18:25:07Z 置信度 0.70
• Locally Weighted Learning
  crossref 2010-12-29T17:28:18Z 置信度 0.70
• Reinforcement Learning
  crossref 2023-07-20T00:06:00Z 置信度 0.70
• SUPERVISED LEARNING
  crossref 2021-05-11T16:24:29Z 置信度 0.70
• Figure 1: YOLOv7-tiny model training process.
  crossref 2024-01-22T03:24:25Z 置信度 0.70
• Introduction to Machine Learning
  crossref Cem Ünsalan, Berkan Höke, Eren Atmaca 2024-10-24T09:02:54Z 置信度 0.70
• Classical Machine Learning Algorithms
  crossref Komaragiri Srinivasa Raju, Dasika Nagesh Kumar 2025-05-21T09:58:14Z 置信度 0.70
• Quantum Classification
  crossref Davide Pastorello 2022-12-16T17:03:00Z 置信度 0.70
• Machine learning–assisted flow velocity analysis in paper microfluidics
  crossref Soo Chung 2024-07-19T06:47:57Z 置信度 0.70
• Machine Learning for Cyber-Physical Power System Security
  crossref Xiaomeng Feng, Yang Liu, Shiyan Hu 2022-04-22T12:17:30Z 置信度 0.70
• Dynamic single machine scheduling using Q-learning agent
  crossref Lian-Fang Kong, Jie Wu 2005-11-08T15:54:34Z 置信度 0.70
• Rule Learning
  crossref Zhi-Hua Zhou 2021-08-20T19:23:05Z 置信度 0.70
• CLASIFICACIÓN DE TEXTOS: UN ENFOQUE CON USO DE MACHINE LEARNING
  La clasificación de textos ha sido utilizada como base para la organización del conocimiento en las más diversas áreas, ya que permite organizar grupos de categorías para guiar el corte de estos dominios. En la era de la información digital, donde existe una g…
  crossref Fábio Eder Cardoso, Edberto Ferneda, Leonardo Botega 2024-03-23T17:05:27Z 置信度 0.70
• Sentiment Analysis of Social Network Comments for Identifying Opinion Leaders Using Machine Learning
  The rate of social media growth is the greatest determinant in the spread of information. In order to understand and change opinion of the majority, it is necessary to find Key Opinion Leaders. In this research, a technique is provided for detecting opinion le…
  crossref Ripunjay Kumar 2026-08-12T14:42:41Z 置信度 0.70
• A Machine Learning‐Based Geothermal Gradient Framework for Constraining Lithospheric Biomass
  Abstract Earth's deep subsurface hosts a large microbial biosphere, but the magnitude of lithospheric biomass remains poorly constrained due to sparse observations and uncertain thermal limits. Here, we present a physically constrained global assessment of the…
  crossref Wenyu Zhao, Harrison B. Smith, J. ZhangZhou 2026-08-18T12:45:08Z 置信度 0.70
• Machine Learning
  crossref I. Bratko 2012-04-16T06:09:39Z 置信度 0.70
• Shallow Learning vs. Deep Learning in Anomaly Detection Applications
  crossref Ismail A. Mageed, Ashiq H. Bhat, Hafeez Ur Rehman 2024-10-12T18:01:28Z 置信度 0.70
• Machine learning modeling methodology for industrial solid ash
  crossref Chongchong Qi, Erol Yilmaz, Qiusong Chen 2024-01-19T09:25:22Z 置信度 0.70
• Guest Editorial: Machine learning in and for music
  crossref G. Widmer 2006-11-28T05:59:39Z 置信度 0.70
• The Design of English Translation Software Based on Machine Learning Technology
  crossref Xiaoshan Zeng 2023-08-24T17:21:09Z 置信度 0.70
• Correction to: Artificial Intelligence and Machine Learning in Healthcare
  crossref Ankur Saxena, Shivani Chandra 2021-07-22T06:03:42Z 置信度 0.70
• Forecasting Using Machine Learning Methods
  crossref Tsung-wu Ho 2025-08-30T16:59:00Z 置信度 0.70
• Contemplation of Photocatalysis Through Machine Learning
  crossref Tulsi Satyavir Dabodiya, Jayant Kumar, Arumugam Vadivel Murugan 2023-05-22T20:35:09Z 置信度 0.70
• Intersection of neutrosophy and machine learning
  crossref Vikram Singh, Gurcharan Dass 2025-03-26T14:20:24Z 置信度 0.70
• Prediction of Epidemic Disease Outbreaks, Using Machine Learning
  crossref Vaishali Gupta, Sanjeev Prasad 2021-05-21T16:57:41Z 置信度 0.70
• KSM Based Machine Learning for Markerless Motion Capture
  A marker-less motion capture system, based on machine learning, is proposed and tested. Pose information is inferred from images captured from multiple (as few as two) synchronized cameras. The central concept of which, we call: Kernel Subspace Mapping (KSM). …
  crossref Therdsak Tangkuampien, David Suter 2010-05-25T19:27:34Z 置信度 0.70
• Machine Learning for Modeling Plant Abiotic Stress Responses
  crossref Haragopal Dutta, Suman Dutta 2025-11-14T20:08:44Z 置信度 0.70
• Comparing Machine Learning Classification Schemes ― a GIS Approach
  crossref A. Lazar, B.A. Shellito 2006-03-22T17:38:08Z 置信度 0.70
• Editorial: Introducing Journal of Machine Learning Advances
  Journal of Machine Learning Advances (JMLA) is a peer-reviewed, open-access journal dedicated to publishing significant, original contributions that advance the theory, methodology, and practice of machine learning. We seek high-impact research that introduces…
  crossref Nianyin Zeng 2026-07-06T08:28:58Z 置信度 0.70
• Case-Based Learning
  crossref 2010-12-29T17:42:10Z 置信度 0.70
• Embodied Evolutionary Learning
  crossref 2010-12-29T17:35:32Z 置信度 0.70
• Compression and federated learning : an approach to frugal machine learning
  Compression et apprentissage fédéré : une approche pour l'apprentissage machine frugal Les appareils et outils “intelligents” deviennent progressivement la norme, la mise en œuvre d'algorithmes basés sur des réseaux neuronaux artificiels se développant largeme…
  crossref Louis Leconte 2026-04-08T17:42:37Z 置信度 0.70
• From challenges to implementation and acceptance: Addressing key barriers in artificial intelligence, machine learning, and deep learning
  Machine learning (ML) and deep learning (DL) have transformed different industries by facilitating sophisticated data analysis, predictive modeling, and autonomous decision-making. Despite the ability to greatly change things, there are many obstacles preventi…
  crossref Nitin Liladhar Rane, Suraj Kumar Mallick, Ömer Kaya, Jayesh Rane 2024-10-22T05:38:03Z 置信度 0.70
• Learning Control Rules
  crossref 2010-12-29T17:28:18Z 置信度 0.70
• Learning in Logic
  crossref 2010-12-29T17:28:18Z 置信度 0.70
• Review for "Study becomes insight: Ecological learning from machine learning"
  crossref 2021-09-10T07:27:33Z 置信度 0.70
• Figure 9: Confusion matrix for machine learning, deep learning, and proposed BERT model.
  crossref 2023-08-03T05:20:36Z 置信度 0.70
• The 6th International Conference on Machine Learning and Machine Intelligence
  crossref 2024-01-16T18:43:15Z 置信度 0.70
• Early Detection of Coronavirus Cases Using Chest X-ray Images Employing Machine Learning and Deep Learning Approaches
  crossref 2020-06-21T01:11:46Z 置信度 0.70
• Tiny machine learning on the edge: A framework for transfer learning empowered unmanned aerial vehicle assisted smart farming
  Abstract Emerging technologies are continually redefining the paradigms of smart farming and opening up avenues for more precise and informed farming practices. A tiny machine learning (TinyML)‐based framework is proposed for unmanned aerial vehicle (UAV)‐assi…
  crossref Ali M. Hayajneh, Sami A. Aldalahmeh, Feras Alasali, Haitham Al‐Obiedollah 等 2023-11-16T08:37:34Z 置信度 0.70
• Machine Learning Paradigms
  crossref Maria Virvou, Efthimios Alepis, George A. Tsihrintzis, Lakhmi C. Jain 2019-03-16T06:04:25Z 置信度 0.70
• Unsupervised Learning of Word Segmentation Rules with Genetic Algorithms and Inductive Logic Programming
  crossref Dimitar Kazakov, Suresh Manandhar 2002-12-22T05:54:50Z 置信度 0.70
• Learning from History for Behavior-Based Mobile Robots in Non-Stationary Conditions
  crossref François Michaud, Maja J. Matarić 2002-12-22T04:48:21Z 置信度 0.70
• The Power of Amnesia: Learning Probabilistic Automata with Variable Memory Length
  crossref Dana Ron, Yoram Singer, Naftali Tishby 2003-11-06T11:45:40Z 置信度 0.70
• Machine Learning and Deep Learning Techniques
  crossref Reena Thakur, Prashant Panse, Parul Bhanarkar 2023-11-01T19:02:31Z 置信度 0.70
• Kernel-Based Machine Translation
  crossref Zhuoran Wang, John Shawe-Taylor 2013-10-10T02:28:14Z 置信度 0.70
• Multiple-Instance Learning
  crossref 2010-12-29T17:30:36Z 置信度 0.70
• Data Acquisition and Preparation for Artificial Intelligence and Machine Learning Applications
  crossref Kallol Bosu Roy Choudhuri, Ramchandra S. Mangrulkar 2021-07-07T18:42:19Z 置信度 0.70
• Learning by Failing to Explain: Using Partial Explanations to Learn in Incomplete or Intractable Domains
  crossref Robert J. Hall 2003-04-04T16:55:36Z 置信度 0.70
• A-iLearn: An adaptive incremental learning model for spoof fingerprint detection
  crossref Shivang Agarwal, Ajita Rattani, C. Ravindranath Chowdary 2021-11-17T04:11:47Z 置信度 0.70
• Elicitation of machine learning to human learning from iterative error correcting
  crossref Juan Gao, Chun-Fang Li, Zhen-Guo Liu, Lian-Zhong Liu 2014-09-10T15:51:21Z 置信度 0.70
• Learning Concepts from Sensor Data of a Mobile Robot
  crossref Volker Klingspor, Katharina J. Morik, Anke D. Rieger 2003-02-06T17:07:14Z 置信度 0.70
• Multimodal StyleFusion: Cross-Attention Learning for Fashion Style Recognition
  crossref Dongmei Han, Shao-Yu Huang, Jiayi Liang, Mohammad Masum 2026-07-23T15:35:50Z 置信度 0.70
• Unsupervised Learning
  crossref Jugal Kalita 2022-11-18T17:36:17Z 置信度 0.70
• Memory-Based Learning
  crossref 2010-12-29T17:30:36Z 置信度 0.70
• Influence of AI and Machine Learning to Empower the Healthcare Sector
  crossref Sumit Koul, Bharti Koul, Bhawna Bakshi 2022-06-27T18:20:26Z 置信度 0.70
• Machine Learning
  crossref 2015-02-21T02:53:16Z 置信度 0.70
• Negative Correlation Learning
  crossref 2010-12-29T17:26:29Z 置信度 0.70
• Quantized Federated Learning
  crossref 2022-06-16T00:05:40Z 置信度 0.70
• Deep learning and machine learning techniques for image classification and analysis
  Η βαθιά μάθηση και η μηχανική μάθηση έχουν μεταμορφώσει ριζικά τον τομέα της ταξινόμησης και ανάλυσης εικόνων, προσφέροντας σημαντικές προόδους στην ιατρική διάγνωση, την περιβαλλοντική και βιομηχανική παρακολούθηση, την αναγνώριση αντικειμένων σε πραγματικό χ…
  crossref Αθανάσιος Καναβός 2026-02-26T07:28:49Z 置信度 0.70
• Deep Learning Game Strategies
  crossref Mark Liu 2023-10-11T11:08:37Z 置信度 0.70
• Phase transitions and relational learning
  crossref 2012-08-08T15:35:08Z 置信度 0.70
• Conclusion: Man, Machine, Metaphor
  crossref 2023-05-23T17:28:58Z 置信度 0.70
• Classification and the Learning Pipeline
  crossref 2022-01-20T00:05:52Z 置信度 0.70
• Reinforcement Learning
  crossref Stephen Marsland 2020-02-13T09:40:07Z 置信度 0.70
• Semantic-Aware Multi-Scale Contrastive Learning for UAV Tiny Object Tracking
  Unmanned aerial vehicle-based visual systems are essential for tasks such as reconnaissance, disaster monitoring, and traffic analysis; however, tracking remains challenging due to tiny targets, background clutter, and interference from similar objects. The au…
  crossref Xuehua Tao, Jiwei Sun 2026-06-08T13:07:24Z 置信度 0.70
• Comparative Deep Learning Architectures to Detect Tiny Features in Ophthalmic Imaging
  crossref Julia Hartmann, Peter Maloca, CéDric Huwyler, Martin Melchior 等 2023-08-01T18:04:03Z 置信度 0.70
• Learning Changing Concepts by Exploiting the Structure of Change
  crossref Peter L. Bartlett, Shai Ben-David, Sanjeev R. Kulkarni 2002-12-22T05:54:50Z 置信度 0.70
• The Future of Machine Learning
  crossref 2019-03-29T19:09:31Z 置信度 0.70
• PAC-MDP Learning
  crossref 2010-12-29T17:26:04Z 置信度 0.70
• Reinforcement Learning
  crossref Zhi-Hua Zhou 2021-08-20T19:23:05Z 置信度 0.70
• Table 9: Comparative performance metrics of traditional, machine learning, and deep learning models.
  crossref 2026-02-25T08:08:57Z 置信度 0.70
• Deep Learning and Machine Learning Techniques for Sustainable Agriculture: A Comprehensive Review
  crossref Anjali Potnis, Rishi Sharma, Vijayshri Chaurasia 2026-07-10T19:36:45Z 置信度 0.70
• Efficient Read-Restricted Monotone CNF/DNF Dualization by Learning with Membership Queries
  crossref Carlos Domingo, Nina Mishra, Leonard Pitt 2002-12-22T05:54:50Z 置信度 0.70
• Introduction to Statistical Machine Learning
  crossref Xiaojin Zhu, Andrew B. Goldberg 2022-06-08T02:39:59Z 置信度 0.70