边缘智能 · 全部实体（第 55 / 100 页）

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• A Comprehensive Review of Machine Learning Privacy
  crossref Haoru Chen 2025-01-08T19:58:48Z 置信度 0.70
• Introduction to Regression Methods
  crossref Filippo MASI 2024-10-08T16:48:48Z 置信度 0.70
• Using Genetic Algorithms for Concept Learning
  crossref Kenneth A. de Jong, William M. Spears, Diana F. Gordon 2003-04-04T16:55:36Z 置信度 0.70
• Unsupervised Learning
  crossref Noel JAKSE 2024-10-08T16:48:48Z 置信度 0.70
• A Novel YOLOv3-tiny Network for Unmanned Airship Obstacle Detection
  crossref Sha Ding, Fei Long, Huijin Fan, Lei Liu 等 2019-11-26T01:26:00Z 置信度 0.70
• ANALYSIS AND CLUSTERING OF STUDENT LEARNING BEHAVIOR USING MACHINE LEARNING
  crossref Sengdeuane Phonthongdy, Phouthone Vongpasith, Sommith Thoummaly, Chitnavanh Phonekhamma 2025-08-21T14:22:53Z 置信度 0.70
• Learning Bayesian Networks: The Combination of Knowledge and Statistical Data
  crossref David Heckerman, Dan Geiger, David M. Chickering 2003-04-04T16:55:36Z 置信度 0.70
• Relationship between Risk Factors of Water Conservancy Project Based on Machine Learning Algorithm
  crossref 2023-03-15T06:40:46Z 置信度 0.70
• Scientific programming with Python
  This chapter offers a brief introduction to scientific programming with Python with an emphasis on some mathematical operations that will form the basis of many algorithms. This will specifically include working with matrices and convolutions. Python is a high…
  crossref Thomas P. Trappenberg 2020-01-23T10:54:46Z 置信度 0.70
• Introducing Microsoft Azure Machine Learning
  crossref Roger Barga, Valentine Fontama, Wee Hyong Tok 2014-12-01T10:54:11Z 置信度 0.70
• Machine Learning
  crossref Pedro Larrañaga, David Atienza, Javier Diaz-Rozo, Alberto Ogbechie 等 2018-12-13T23:09:24Z 置信度 0.70
• Ensemble Methods in Machine Learning: Boosting, Bagging, and Stacking for Enhanced Model Performance
  Ensemble methods, particularly boosting, bagging, and stacking, have revolutionized machine learning by enhancing model performance through advanced aggregation techniques. This chapter provides an in-depth exploration of these methods, with a focus on novel a…
  crossref Supriya Devi 2025-06-04T07:10:26Z 置信度 0.70
• Applications of Machine Learning
  crossref 2024-03-22T14:00:37Z 置信度 0.70
• A review of machine learning techniques in cybersecurity and research opportunities
  crossref Sangeeta Mittal 2022-11-22T13:56:53Z 置信度 0.70
• Training Invariant Support Vector Machines
  crossref Dennis Decoste, Bernhard Schölkopf 2002-12-23T08:38:06Z 置信度 0.70
• Machine learning algorithms in the context of digital education
  crossref Cuiping Ma 2026-08-14T14:55:20Z 置信度 0.70
• An Integration of Rule Induction and Exemplar-Based Learning for Graded Concepts
  crossref Jianping Zhang, Ryszard S. Michalski 2003-04-04T16:55:36Z 置信度 0.70
• Machine Learning-Based Optimal Wi-Fi HaLow Standard for Dense IoT Networks
  crossref M. Mahesh, V.P. Harigovindan 2021-08-12T13:05:23Z 置信度 0.70
• Two Contributions of Constraint Programming to Machine Learning
  crossref Arnaud Lallouet, Andreï Legtchenko 2005-11-09T06:54:27Z 置信度 0.70
• Tiny Forest Zaanstad : citizen science and determining biodiversity in Tiny Forest Zaanstad
  crossref Fabrice Ottburg, Dennis Lammertsma, Jaap Bloem, Wim Dimmers 等 2018-04-20T07:30:22Z 置信度 0.70
• StreamNet: Memory-Efficient Streaming Tiny Deep Learning Inference on the Microcontroller
  crossref Hong-Sheng Zheng, Yu-Yuan Liu, Chen-Fong Hsu, Tsung Tai Yeh 2026-03-02T13:18:04Z 置信度 0.70
• Milne, Denys Gordon, (Tiny), (12 Jan. 1926–9 Feb. 2000)
  crossref 2017-11-30T07:17:33Z 置信度 0.70
• Advanced Optimization Algorithms and Mathematical Intelligence for Machine Learning-Based Engineering Systems
  The integration of advanced optimization algorithms and mathematical intelligence has revolutionized the design and operation of modern engineering systems. This chapter explores the synergies between optimization techniques and machine learning, focusing on t…
  crossref A Thangam, Vijay Kumar Dwivedi 2026-05-13T04:03:28Z 置信度 0.70
• Code2Drive: A Code-based Interactive and Educational Driving Environment for Improving the Youth Driving Learning and Training using Machine Learning
  Serious games are video games designed for more than just pure entertainment purposes [1]. Serious games developers combine traditional game mechanics and the ideas to educate, inform and facilitate social change [2]. These games can be used in many occasions,…
  crossref Zihao Lin, Jonathan Sahagun 2023-09-02T16:25:26Z 置信度 0.70
• Applications of Swarm Intelligence and Machine Learning for COVID-19
  crossref Anurag, J Naren 2022-07-05T00:14:11Z 置信度 0.70
• Effective and Efficient Knowledge Base Refinement
  crossref Leonardo Carbonara, Derek Sleeman 2002-12-22T05:54:50Z 置信度 0.70
• Machine Learning Approach to Search Query Classification
  Search query classification is a necessary step for a number of information retrieval tasks. This chapter presents an approach to non-hierarchical classification of search queries that focuses on two specific areas of machine learning: short text classificatio…
  crossref Isak Taksa, Sarah Zelikovitz, Amanda Spink 2011-10-04T09:46:18Z 置信度 0.70
• Machine Learning Challenges in Chemoinformatics and Drug Screening and Design
  crossref Pierre Baldi 2008-04-28T17:34:00Z 置信度 0.70
• Hedonic Housing Theory — A Machine Learning Investigation
  crossref Timothy Oladunni, Sharad Sharma 2017-02-07T20:39:53Z 置信度 0.70
• Characteristic Sets for Polynomial Grammatical Inference
  crossref Colin de la Higuera 2002-12-22T04:48:21Z 置信度 0.70
• Dimensionality Reduction Methods in Machine Learning
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Data Analytics and Compliance in Cloud-Machine Learning
  crossref Seema Rawat, Praveen Kumar 2025-07-29T15:15:48Z 置信度 0.70
• An Intelligent Food Inventory Monitoring System using Machine Learning and Computer Vision
  Due to technological advancements, humans are able to produce more food than ever before. In fact, the food production level is so high that all population could be supported if the food resource is distributed correctly. Yet, it is more than common to see ite…
  crossref Tianyu Li, Yu Sun 2022-09-19T07:46:03Z 置信度 0.70
• Ein experimenteller Vergleich von Batch- und Online-Machine-Learning-Algorithmen
  crossref Thomas Bartz-Beielstein 2024-07-22T17:02:37Z 置信度 0.70
• Industrial Applications of Machine Learning
  crossref Patanjali Kashyap 2023-12-22T12:03:36Z 置信度 0.70
• Ein experimenteller Vergleich von Batch- und Online Machine Learning-Algorithmen
  crossref Thomas Bartz-Beielstein 2024-09-19T14:13:15Z 置信度 0.70
• Intelligent Patrol Robot Based on Visual Machine Deep Learning
  crossref Lirui Liu 2022-02-15T20:54:54Z 置信度 0.70
• Introducing Machine Learning
  crossref Christopher M. Rosett, Austin Hagerty 2021-06-14T09:02:48Z 置信度 0.70
• Introduction
  crossref Davide Pastorello 2022-12-16T17:03:00Z 置信度 0.70
• Machine Learning in Diagnosis of Children with Disorders
  crossref Lokesh Kumar Saxena, Manishikha Saxena 2020-10-06T08:47:09Z 置信度 0.70
• The practice on using machine learning for network anomaly intrusion detection
  crossref Yu-Xin Meng 2011-09-21T16:16:22Z 置信度 0.70
• Survival Probability Assessment using Machine Learning Algorithms
  crossref Jingyi Wang, Boyang Zhang 2022-11-15T20:45:01Z 置信度 0.70
• Introduction
  crossref Zhiyuan Chen, Bing Liu 2022-12-06T14:08:30Z 置信度 0.70
• Machine Learning-Based Aging Analysis
  crossref Arunkumar Vijayan, Krishnendu Chakrabarty, Mehdi B. Tahoori 2019-03-15T12:32:24Z 置信度 0.70
• Simple rules outperform machine learning for personnel selection: insights from the 3rd annual SIOP machine learning competition
  Abstract Machine learning (ML) algorithms are often assumed to be the most accurate way of producing pre-dictive models despite problems with explainability and adverse impact. The 3rd annual Society for Industrial and Organizational Psychology Machine Learnin…
  crossref Jason L. Harman, Jaelle Scheuerman 2022-09-14T21:30:47Z 置信度 0.70
• 2024 5th International Conference on Machine Learning and Computer Application (ICMLCA)
  crossref 2024-11-21T19:04:04Z 置信度 0.70
• Khiops: A Statistical Discretization Method of Continuous Attributes
  crossref Marc Boulle 2004-03-15T22:58:02Z 置信度 0.70
• Neural networks and Keras
  This chapter discusses the basic operation of an artificial neural network which is the major paradigm of deep learning. The name derives from an analogy to a biological brain. The discussion begins by outlining the basic operations of neurons in the brain and…
  crossref Thomas P. Trappenberg 2020-01-23T10:56:00Z 置信度 0.70
• Machine Learning for Materials Science
  crossref Bharat K. Jasthi, Venkataramana Gadhamshetty, Grigoriy A. Sereda, Alexey Lipatov 2023-12-04T12:44:20Z 置信度 0.70
• Analysis of Stock Market Quantitative Trading Strategies Based on Machine Learning
  crossref Tiange Tian 2023-03-07T18:42:09Z 置信度 0.70
• Variance and Bias for General Loss Functions
  crossref Gareth M. James 2003-04-04T16:57:10Z 置信度 0.70
• On the Worst-Case Analysis of Temporal-Difference Learning Algorithms
  crossref Robert E. Schapire, Manfred K. Warmuth 2003-02-06T17:07:14Z 置信度 0.70
• Aligning with the Business
  crossref Eric Carter, Matthew Hurst 2019-08-21T11:03:53Z 置信度 0.70
• Machine Learning and Neglected Tropical Diseases
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Optimization Scheme of Accurate Calculation Algorithm of Electric Charge Based on Machine Learning
  crossref 2023-03-15T06:40:46Z 置信度 0.70
• Machine Learning
  crossref Elham Ghanbari, Sara Najafzadeh 2020-08-29T14:09:59Z 置信度 0.70
• Adversarial Machine Learning in Cybersecurity
  crossref Tony Thomas, Athira P. Vijayaraghavan, Sabu Emmanuel 2019-12-16T22:02:52Z 置信度 0.70
• Advancing magnetic material discovery through machine learning: Unveiling new manganese-based materials
  Magnetic materials are used in a variety of applications, such as electric generators, speakers, hard drives, MRI machines, etc. Discovery of new magnetic materials with desirable properties is essential for advancement in these applications. In this research …
  crossref Yogesh Khatri, Arti Kashyap 2023-12-01T14:17:30Z 置信度 0.70
• The Use of Machine Learning Techniques to Classify Content on the Web
  In text categorization, texts are sorted into groups according to their content. It is the process of automatically classifying texts written in natural languages according to a set of guidelines. Both text comprehension systems, which perform transformations …
  crossref Dikshit Sharma 2025-02-12T11:53:51Z 置信度 0.70
• Performance Evaluation of Advanced Machine Learning Algorithms for Surface Defect Detection
  crossref Boxu Zhu 2026-01-22T20:58:30Z 置信度 0.70
• Applied Machine Learning for Health and Fitness
  crossref Kevin Ashley 2020-08-24T08:03:59Z 置信度 0.70
• Nonparametric Time Series Prediction Through Adaptive Model Selection
  crossref Ron Meir 2002-12-22T05:54:50Z 置信度 0.70
• Integrating Machine Learning Techniques for Comprehensive Malware Classification
  Malware is a global problem. Malware's impact according to studies is escalating. In the fight against malware, Malware detection and analysis techniques are the most important defense tools. That which makes a detector good, or terrible, is largely determined…
  crossref Sridevi, T. K. Gundoor, Rajeev Mulimani 2025-04-16T11:55:06Z 置信度 0.70
• Preface: A Brief History of Why Machine Learning Projects Stall
  crossref 2024-02-06T19:44:47Z 置信度 0.70
• Cell Fate Analysis and Machine Learning
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Empirical Risk Minimization
  crossref Alexander Jung 2022-01-21T09:03:38Z 置信度 0.70
• Bayesian Clustering by Dynamics
  crossref Marco Ramoni, Paola Sebastiani, Paul Cohen 2002-12-23T17:11:47Z 置信度 0.70
• Machine Learning in Multiple Sclerosis
  Abstract Multiple sclerosis (MS) is characterized by inflammatory activity and neurodegeneration, leading to the accumulation of damage to the central nervous system resulting in the accumulation of disability. MRI depicts an important part of the pathology of…
  crossref Bas Jasperse, Frederik Barkhof 2023-07-24T19:03:02Z 置信度 0.70
• Deep Learning Detection of Tiny Wood Splinters on Gymnasium Floor
  crossref Koji Saisho, Alberto Petrilli, Shigeki Sumiya, Masataka Yamamoto 等 2024-01-29T18:32:04Z 置信度 0.70
• Study of Biomarker and Machine Learning
  crossref Shyamasree Ghosh, Rathi Dasgupta 2022-05-04T17:03:22Z 置信度 0.70
• Corporate Social Responsibility & Machine Learning "Leveraging Machine Learning to Predict the Impact of CSR Initiatives on Corporate Financial Performance"
  crossref Musferah Musferah 2025-06-19T20:23:25Z 置信度 0.70
• Enhancing Allergy Prediction Accuracy Through Machine Learning and ProteinBERT
  crossref Agastya Chennamsetty, Atul Dubey 2025-03-04T18:39:11Z 置信度 0.70
• Enhancing Brain Tumor Image Classification with Cost-Effective Machine Learning Techniques
  crossref Haoling Xie 2026-01-22T20:58:30Z 置信度 0.70
• Challenges and ethical considerations in implementing Machine Learning
  crossref Mariam Khaled Galal, Zafar Said 2026-06-26T13:32:30Z 置信度 0.70
• Boosting and Other Machine Learning Algorithms
  crossref Harris Drucker, Corinna Cortes, L.D. Jackel, Yann LeCun 等 2014-07-01T02:58:38Z 置信度 0.70
• Trading places: What happens when neuroscience turns into machine learning, and machine learning turns into neuroscience?
  crossref Samuel Gershman 2026-03-30T19:56:33Z 置信度 0.70
• Machine Learning and Deep Learning for Multimodal Biometrics
  crossref Danvir Mandal, Shyam Sundar Pattnaik 2023-10-17T06:45:46Z 置信度 0.70
• DECISION MAKING IN SCIENTIFIC MACHINE LEARNING
  Scientific Machine Learning is built on the science-of-counting, is deductively solvable, and well-suited to business and human applications that naturally count. From the Gibbs formalism, Scientific Machine Learning produces unique and exact scientific measur…
  crossref Mark Temple-Raston 2026-04-02T09:54:07Z 置信度 0.70
• Markov Processes on Curves
  crossref Lawrence K. Saul, Mazin G. Rahim 2002-12-22T05:54:50Z 置信度 0.70
• Introduction to Nonstationary Signal Analysis and Machine Learning
  crossref 2022-03-29T22:31:43Z 置信度 0.70
• A Non-technical Introduction to Machine Learning
  Abstract This chapter provides an introduction to machine learning for a non-technical readership. Machine learning is an approach to artificial intelligence. The chapter thus starts with a brief history of artificial intelligence in order to put machine learn…
  crossref Olivier Colliot 2023-07-24T19:03:02Z 置信度 0.70
• A Quick Review of Machine Learning Algorithms
  crossref Susmita Ray 2019-10-10T19:12:41Z 置信度 0.70
• MACHINE LEARNING MODELS FOR PREDICTING EMPLOYEE RETENTION AND PERFORMANCE
  This paper examines the usage of machine learning models in forecasting performance and retention among employees, important organizational performance elements. Both substandard performance and high turnover are expensive, and in turn, insights based on data …
  crossref Nishitha Reddy Nalla 2025-02-28T12:43:01Z 置信度 0.70
• Machine Learning in Medical Systems
  Abstract Medicine and healthcare are crucial areas in the application of machine learning (ML) and artificial intelligence (AI). While ML promises to revolutionize healthcare, it also raises various social, ethical, and regulatory issues as well as novel socio…
  crossref Wanheng Hu 2024-05-22T15:25:51Z 置信度 0.70
• Machine learning-based risk prediction of corporate digital transformation
  crossref Jin Xie 2026-08-14T14:55:03Z 置信度 0.70
• A Comparative Study of Classification Algorithms: Statistical, Machine Learning and Neural Network
  Abstract The aim of the Stat Log project is to compare the performance of statistical, machine learning, and neural network algorithms, on large real world problems. This paper describes the completed work on classification in the Stat Log project. Classificat…
  crossref R D King, R Henery, C Feng, A Sutherland 2023-11-02T13:23:57Z 置信度 0.70
• Penerapan Microexpressions Untuk Mengenali Hubungan Kekerabatan Menggunakan Extreme Learning Machine
  Dalam dunia computer vision, riset tentang ekspresi wajah sudah dicoba oleh Chinese Academy of Sciences MicroExpression (CASME). Riset tersebut membuat basis informasi yang ada sebagian foto ekspresi wajah yang bertujuan buat menolong riset di bidang computer …
  crossref Rizqi Ramadhan, Ike Fibriani, Widya Cahyadi 2022-12-14T14:58:27Z 置信度 0.70
• Solving the Mixed‐model Assembly Line Balancing Problem by using a Hybrid Reactive Greedy Randomized Adaptive Search Procedure
  crossref Belkharroubi Lakhdar, Khadidja Yahyaoui 2022-02-25T22:49:03Z 置信度 0.70
• Comparing deep reinforcement learning architectures for autonomous racing
  crossref Benjamin David Evans, Hendrik Willem Jordaan, Herman Arnold Engelbrecht 2023-09-04T19:32:25Z 置信度 0.70
• SafePredict: A Machine Learning Meta-Algorithm That Uses Refusals to Guarantee Correctness
  SafePredict is a novel meta-algorithm that works with any base prediction algorithm for online data to guarantee an arbitrarily chosen correctness rate, 1−ϵ, by allowing refusals. Allowing refusals means that the meta-algorithm may refuse to emit a prediction …
  crossref David Ramirez 2022-10-17T23:42:15Z 置信度 0.70
• Fair Machine Learning Through the Lens of Causality
  crossref Yongkai Wu, Lu Zhang, Xintao Wu 2023-11-25T13:02:19Z 置信度 0.70
• Machine Learning and AI Ethics
  crossref Arjun Panesar 2020-12-15T14:05:55Z 置信度 0.70
• Hybrid Machine Learning Approach in Data Mining
  crossref Jyothi Bellary, Bhargavi Peyakunta, Sekhar Konetigari 2010-05-12T20:45:22Z 置信度 0.70
• Data Pipeline Design for Machine Learning
  crossref Mohammad Reza Mahdiani 2026-04-30T22:20:08Z 置信度 0.70
• Capacities of Some Other Machine Learning Methods
  crossref Gerald Friedland 2023-12-01T06:04:04Z 置信度 0.70
• Machine Learning for Optimal Resource Allocation
  crossref Marius Pesavento, Florian Bahlke 2019-12-13T21:26:07Z 置信度 0.70
• Efficient Incremental Induction of Decision Trees
  crossref Dimitrios Kalles, Tim Morris 2003-02-06T17:07:14Z 置信度 0.70
• Optimally pruned extreme learning machine: A new nontuned machine learning model for predicting chlorophyll concentration
  crossref Salim Heddam 2023-01-27T11:57:43Z 置信度 0.70
• Linear-Algebra Based Quantum Machine Learning
  crossref 2020-07-15T10:43:29Z 置信度 0.70
• Machine Learning and the Analysis of Culture
  Abstract The focus of this chapter is on how machine learning (ML) affects the analysis of culture in sociology. It shows how ML has greatly advanced the analysis of culture with new tools that enable a massive, fine-grained extraction of information from text…
  crossref Sophie Mützel, Étienne Ollion 2024-06-20T16:27:54Z 置信度 0.70