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• Detecting moving objects with machine learning
  crossref Wesley C. Fraser 2024-11-22T05:27:38Z 置信度 0.70
• Introduction to Machine Learning and Tiny Machine Learning
  Machine learning (ML) is one of the key components of artificial intelligence (AI). Basically AI is composed of two major components, fuzzy inference system (FIS) and ML. Before we can continue on this topic, first let s try to answer a question, what is ML?
  crossref Ying Bai 2026-08-27T18:04:11Z 置信度 0.70
• Marian-Andrei Rizoiu Discusses Blending Digital Ethnography and Advanced Machine Learning
  crossref 2022-07-13T13:21:36Z 置信度 0.70
• An Overview of Machine Learning
  Machine learning is a field in computer science where data are used to predict, or respond to, future data. It is closely related to the fields of pattern recognition, computational statistics, and artificial intelligence. The data may be historical or updated…
  crossref Michael Paluszek, Stephanie Thomas 2019-01-31T14:23:16Z 置信度 0.70
• Why Machine Learning?
  While both machine learning and numerical simulations have merits and demerits, machine learning models could be preferred over computational approach due to the following reasons. The adaptability of machine learning algorithm models to an unseen and novel da…
  crossref Oluwatobi Adeleke, Sina Karimzadeh, Tien-Chien Jen 2023-10-31T14:25:44Z 置信度 0.70
• Quantum Machine Learning Approaches
  The rise of machine learning algorithms in multidisciplinary areas is inevitable. Quantum computers are a significant recent area of development where complex algorithms can be efficiently executed for better performance. Implementing complex algorithms in qua…
  crossref Mangalraj Poobala, Ganesh Kumar Natarajan, Iniyan Shanmugam, Justin Vargese 2024-10-01T19:47:03Z 置信度 0.70
• Clausal Discovery
  crossref Luc De Raedt, Luc Dehaspe 2002-12-22T04:48:21Z 置信度 0.70
• Shallow Learning Versus Deep Learning in Biomedical Applications
  crossref Mithat Önder, Ümit Şentürk, Kemal Polat 2024-10-12T14:01:28Z 置信度 0.70
• Self bounding Genetic Algorithms for Machine Learning
  We propose an abstract self bounding genetic algorithm that can be applied to various problems of machine learning. The bound on the generalization error that is output by our algorithm is based on Rademacher penalization, a data driven penalization technique.…
  crossref F. Lozano, V. Koltchinskii 2006-03-22T17:38:08Z 置信度 0.70
• Machine Learning for Secure Hardware Design
  crossref Mohd Syafiq Mispan, Basel Halak 2022-04-22T12:17:30Z 置信度 0.70
• Predictive Analysis of Campus Placement of Student Using Machine Learning Algorithms
  In today's competitive job market, student campus placement process becomes very important role in the academic journey of students and the reputation of educational institutions alike. As the demand for skilled professionals continues to rise, the ability to …
  crossref 2024-05-17T10:51:00Z 置信度 0.70
• Machine Learning-Base Drug Repurposing for Amyotrophic Lateral Sclerosis (ALS)
  This study presents a machine learning-based framework for drug repurposing with open source datasets, leveraging cheminformatics and generative modeling to identify compounds with potential therapeutic relevance for neurodegenerative diseases such as Amyotrop…
  crossref Hannah Archer 2025-05-18T16:45:12Z 置信度 0.70
• Machine Learning based Health Status Assessment of Super-span Suspension Bridges
  With the rapid development of bridge design and analysis theory, building materials development and construction technology, more and more long-span bridges are built across rivers and seas.At the same time, the bridge construction scheme of over 1000 meters o…
  crossref 2023-02-06T01:35:28Z 置信度 0.70
• Ship Lock Electromagnetically Remote Fault Diagnosis Mode Based on Machine Learning
  The traditional electromechanical fault diagnosis mode of the ship lock adopts the wired connection control intelligent control system, which cannot be remotely and wirelessly controlled through the control field and more strict control mode.The design of ship…
  crossref 2023-02-06T03:45:12Z 置信度 0.70
• IBM Parallel Machine Learning Toolbox
  In many ways, the objective of the IBM Parallel Machine Learning Toolbox (PML) is similar to that of Google's MapReduce programming model (Dean and Ghemawat, 2004) and the open source Hadoop system, which is to provide Application Programming Interfaces (APIs)…
  crossref Edwin Pednault, Elad Yom-Tov, Amol Ghoting 2012-02-06T06:03:53Z 置信度 0.70
• Continuous Delivery
  The Agile principle for this chapter describes how frequently a team delivers working software. We extend this to include frequently delivering data as much of what a data engineering team delivers is high-quality data. We further extend the principle by repla…
  crossref Eric Carter, Matthew Hurst 2019-08-21T11:03:53Z 置信度 0.70
• Machine Learning and Its Equivalence to Statistical Data Assimilation
  crossref 2022-01-27T00:07:10Z 置信度 0.70
• Clustering
  crossref Alexander Jung 2022-01-21T09:03:38Z 置信度 0.70
• Automatic Head Count Based on Machine Learning in Intelligent Video Surveillance
  With the wide application of video surveillance system, visual information has become the key research element of modern security technology.Computer vision related technology can be applied to the field of intelligent surveillance, so that computers can proce…
  crossref 2023-02-06T05:13:47Z 置信度 0.70
• Introduction to Machine Learning and Its Applications to Neuroscience
  In the rapidly evolving landscape of scientific inquiry, the convergence of machine learning (ML) and neuroscience has ignited transformative synergy, redefining the frontiers of knowledge acquisition and application. This chapter succinctly encapsulates the p…
  crossref Wasswa Shafik 2025-02-14T12:21:05Z 置信度 0.70
• Brain-Inspired Machine Learning Algorithm: Neural Network Optimization
  crossref Khaled Salah Mohamed 2018-03-02T01:51:51Z 置信度 0.70
• Hungary’s Corporate Sector Risk: a Machine Learning Aproach
  crossref 2026-07-13T05:42:19Z 置信度 0.70
• Adversarial Machine Learning
  crossref Yevgeniy Vorobeychik, Murat Kantarcioglu 2022-06-08T12:29:30Z 置信度 0.70
• Machine Learning in EDA: When and How
  Machine learning is a powerful technique that can derive knowledge from large data set, and provide prediction and modeling. Since VLSI chip designs have extremely high complexity and gigantic data, recently there has been a surge in applying and adapting mach…
  crossref Bei Yu 2023-10-31T13:50:10Z 置信度 0.70
• Techniques and optimization algorithms in machine learning: A review
  Machine learning (ML) has transformed different sectors by allowing for data-based decision-making and forecasting analysis. This study explores the most recent techniques and algorithms that are influencing the direction of machine learning. The research inve…
  crossref Nitin Liladhar Rane, Suraj Kumar Mallick, Ömer Kaya, Jayesh Rane 2024-10-21T11:52:16Z 置信度 0.70
• Regularization
  crossref Alexander Jung 2022-01-21T09:03:38Z 置信度 0.70
• Introduction to Machine Learning
  This chapter provides a brief introduction to the field of machine learning, a subfield of artificial intelligence and pattern recognition. Machine learning techniques are becoming ubiquitous in modern technology. One of the simplest tasks for machine learning…
  crossref Jeffrey Paul Wheeler 2023-10-28T01:58:30Z 置信度 0.70
• Machine learning for the estimation of affective dimensions
  crossref Markus Kächele 2019-11-19T12:04:24Z 置信度 0.70
• Machine learning for health care
  Machine learning (ML) is predominately being used to solve various technical and nontechnical challenges around the world. Taking the context of health care, ML took over the control and helping many practitioners for effective decision-making. Practically, th…
  crossref B. K. Kumar Raju Alluri 2020-12-22T03:07:36Z 置信度 0.70
• Fundamentals of Machine Learning
  crossref Joel Mathew Cherian, Ravindra Kumar 2023-06-21T12:02:49Z 置信度 0.70
• Semantic Data Types in Machine Learning from Healthcare Data
  Healthcare is particularly rich in semantic information and background knowledge describing data. This paper discusses a number of semantic data types that can be found in healthcare data, presents how the semantics can be extracted from existing sources inclu…
  crossref Janusz Wojtusiak 2013-01-17T15:36:00Z 置信度 0.70
• Machine Learning Algorithm for Wireless Indoor Localization
  Smartphones equipped with Wi-Fi technology are widely used nowadays. Due to the need for inexpensive indoor positioning systems (IPSs), many researchers have focused on Wi-Fi-based IPSs, which use wireless local area network received signal strength (RSS) data…
  crossref Osamah Ali Abdullah, Ikhlas Abdel-Qader 2018-09-20T07:16:02Z 置信度 0.70
• Machine Learning-based High-Dimensional Text Document Classification and Clustering
  Text classification is a difficult technique. Many techniques have been developed to decrease the dimension of feature vectors for use in text classification due to their enormous size. This work provides a detailed discussion of unique parameters utilising an…
  crossref Ansh Kataria 2025-02-12T11:53:51Z 置信度 0.70
• Editorial: The terminology of machine learning
  crossref Pat Langley 2004-11-04T01:07:55Z 置信度 0.70
• Technical Excellence
  When we think of agility, we think of efficiency of movement and an ability to change direction in the face of a complex and changing terrain all in the pursuit of a specific goal. Usain Bolt running 100 meters in 9.58 seconds is impressive, but it isn’t an ex…
  crossref Eric Carter, Matthew Hurst 2019-08-21T11:03:53Z 置信度 0.70
• Machine Learning-Driven Digital Technologies for Educational Innovation [Front matter]
  Conference proceedings front matter may contain various advertisements, welcome messages, committee or program information, and other miscellaneous conference information. This may in some cases also include the cover art, table of contents, copyright statemen…
  crossref 2022-03-17T16:54:04Z 置信度 0.70
• A Parallel Graph Coloring Algorithm with Applications in Machine Learning
  In this paper, we present a parallel algorithm for coloring 3-colorable graphs, based on Wigderson’s classical algorithm which guarantees a coloring using at most O( √ n) colors. To parallelize the approach, we integrate Luby and Gebremedhin-Manne (GM) algorit…
  crossref Maedeh Yahaghi, Saeed Bakhshan 2026-02-20T09:59:04Z 置信度 0.70
• Machine learning and soil sciences: A review aided by machine learning tools
  Abstract. The application of machine learning (ML) techniques in various fields of science has increased rapidly, especially in the last ten years. The increasing availability of soil data that can be efficiently acquired remotely and proximally, and freely av…
  crossref José Padarian, Budiman Minasny, Alex B. McBratney 2019-09-03T06:19:24Z 置信度 0.70
• Introduction to Machine Learning
  When we are born, we are incapable of doing anything. We can’t even hold our head straight at that time, but eventually we start learning. Initially we all fumble, make tons of mistakes, fall down, and bang our head many times but slowly learn to sit, walk, ru…
  crossref Pramod Singh 2018-12-14T12:45:15Z 置信度 0.70
• The changing science of machine learning
  crossref Pat Langley 2011-02-17T19:16:54Z 置信度 0.70
• Attacking and Defending Deep Learning
  crossref Yevgeniy Vorobeychik, Murat Kantarcioglu 2022-06-09T04:48:23Z 置信度 0.70
• Role of Artificial Intelligence and Machine Learning in Schizophrenia—A Survey
  Schizophrenia (SZ) is a neurological condition that begins before birth and usually manifests itself in preadolescence. It exhibits a range of pre-morbid and pro-dromal symptoms, progressing to a full-blown psychotic syndrome. Patients with SZ require continua…
  crossref Bhawana Paliwal, Khandakar F. Rahman 2023-02-08T09:25:10Z 置信度 0.70
• Evaluation of Bio-Inspired Algorithm-based Machine Learning and Deep Learning Models
  Natural processes such as neural networks, swarm intelligence, and genetic development have inspired algorithms that exhibit great promise in solving complicated optimization problems in deep learning (DL) and machine learning (ML). A thorough evaluation of bi…
  crossref Selvam Durairaj, Malik Mohamed Umar, B. Natarajan 2025-01-02T15:33:02Z 置信度 0.70
• Self-Directed Learning and Its Relation to the VC-Dimension and to Teacher-Directed Learning
  crossref Shai Ben-David, Nadav Eiron 2002-12-22T05:04:10Z 置信度 0.70
• Machine Learning for Big Data Analytics, Interactive and Reinforcement
  The objective of this chapter is to provide a concise view on machine learning (ML) for big data analytics and to enlighten the reader about interactive reinforcement ML. The big data revolution promises to transform life, work, and thought by enabling process…
  crossref Ritwik Raj, Anjana Mishra 2020-10-09T12:43:22Z 置信度 0.70
• Algorithms and Lower Bounds for On-Line Learning of Geometrical Concepts
  crossref Wolfgang Maass, György Turán 2003-04-04T16:55:36Z 置信度 0.70
• Distributed Optimization in Machine Learning
  crossref Gauri Joshi 2022-11-25T17:57:29Z 置信度 0.70
• The Complexity of Learning According to Two Models of a Drifting Environment
  crossref Philip M. Long 2002-12-22T05:54:50Z 置信度 0.70
• Machine Learning in Evidence Synthesis Research
  crossref Alonso Carrasco-Labra, Olivia Urquhart, Heiko Spallek 2021-07-24T09:02:42Z 置信度 0.70
• Machine Learning Algorithms and Techniques
  crossref Deepti Chopra, Roopal Khurana 2026-04-29T10:18:46Z 置信度 0.70
• Theory and Practice of Super Parameter Optimization for Machine Learning Algorithm
  Before the rise of large machine learning algorithms, most people manually adjusted the super parameters of the model by relying on experience.However, with the increasing complexity of the model, this method obviously cannot meet the needs.This paper mainly s…
  crossref 2023-02-06T01:35:28Z 置信度 0.70
• Machine Learning for All – Introducing Machine Learning in K-12
  Although Machine Learning (ML) is integrated today into various aspects of our lives, few understand the technology behind it. This presents new challenges to extend computing education early on including ML concepts in order to help students to understand its…
  crossref Christiane Gresse von Wangenheim, Lívia S. Marques, Jean C. R. Hauck 2020-08-28T10:26:14Z 置信度 0.70
• Machine learning in remote sensing data processing
  Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmenta…
  crossref Gustavo Camps-Valls 2009-11-05T13:41:43Z 置信度 0.70
• Learning and Revising User Profiles: The Identification of Interesting Web Sites
  crossref Michael Pazzani, Daniel Billsus 2002-12-22T04:48:21Z 置信度 0.70
• Chapter 1: Introduction to Machine Learning in the Geosciences
  crossref 2024-12-18T22:35:18Z 置信度 0.70
• Machine Learning in Drug Discovery
  A drug is a substance that when put into the body can change the way the body works and a person's mental state. Discovering the accurate drug plays a vital role in saving precious lives. In traditional drug creation, scientists identify a target in the body a…
  crossref Deepak B 2023-03-21T09:58:20Z 置信度 0.70
• Machine Learning‐Driven Precision Plant Breeding
  Despite remarkable progress in plant science, sustainable crop enhancement and global food security continue to rely on innovative breeding strategies. Early breakthroughs involved genetic markers, which dramatically enhanced the identification and selection o…
  crossref Krishna Kumar Rai 2025-11-14T20:08:44Z 置信度 0.70
• Randomizing Outputs to Increase Prediction Accuracy
  crossref Leo Breiman 2002-12-22T05:54:50Z 置信度 0.70
• Basics of Machine Learning
  The concept of machine learning is broad. The term “artificial intelligence (AI)” first appeared in 1956. AI refers to systems or software on computers designed to mimic intelligent tasks typically performed by humans, such as understanding spoken language, ma…
  crossref Tsutomu T. Takeuchi 2025-02-27T17:49:36Z 置信度 0.70
• Stock Market Predictability Using Machine Learning Techniques
  The purpose of this article is to examine stock market analysis using machine learning techniques unique and attractive way to reassess products according to the changing times, and to publish information to create textual information from one or more sources …
  crossref Jiuye Wu 2022-11-15T20:45:01Z 置信度 0.70
• Parameter Estimation in Stochastic Logic Programs
  crossref James Cussens 2002-12-22T23:10:54Z 置信度 0.70
• Robotic Process Automation in Business Management Using Machine Learning Algorithms
  Robotic process automation bots try to mimic how people communicate with computers. RPA bots may handle activities that include document scanning and conversion into a machine-readable format thanks to optical character recognition. Most document automation ta…
  crossref P. Gayathiri 2025-02-26T13:51:14Z 置信度 0.70
• Using Iterated Bagging to Debias Regressions
  crossref Leo Breiman 2002-12-30T09:36:44Z 置信度 0.70
• Optimization of Machine Learning Models and Application Supported by Data Engineering
  With the rapid progress of intelligent technology, machine learning is increasingly widely used in all walks of life, and the role of data engineering is increasingly prominent, becoming the core link that determines the efficiency and practicability of the mo…
  crossref 2026-01-12T09:54:59Z 置信度 0.70
• New theoretical directions in machine learning
  crossref David Haussler 2004-10-31T02:00:05Z 置信度 0.70
• 5th International Conference on Machine Learning and Applications-Copyright
  Copyright and Reprint Permissions: Abstracting is permitted with credit to the source. Libraries may photocopy beyond the limits of US copyright law, for private use of patrons, those articles in this volume that carry a code at the bottom of the first page, p…
  crossref 2011-06-08T12:59:25Z 置信度 0.70
• Machine Learning Enhanced Network Intrusion Detection System
  Contemporary cybersecurity demands have elevated network protection to a fundamental requirement for any computational system. Safeguarding networks from unauthorized infiltration is essential for maintaining seamless operational continuity in advanced network…
  crossref Muhammad Yasir Iqbal 2026-01-03T12:38:48Z 置信度 0.70
• Machine Learning Algorithms
  You do not need a background in algebra and statistics to get started in machine learning. The previous chapter introduced key foundation principles. Be under no illusions; mathematics is a huge part of machine learning. Mathematics is key to understanding how…
  crossref Arjun Panesar 2020-12-15T14:05:55Z 置信度 0.70
• Basics of Machine Learning for Brain Signal Decoding
  This chapter covers the basic concepts of machine learning (ML) related to brain signal decoding, pointing out its significance in understanding neural activity and for applications such as BCI, neurofeedback, and cognitive monitoring. It describes different m…
  crossref Richa, Sakshi Mittal 2026-08-14T21:23:06Z 置信度 0.70
• Proceedings 2022 Asia Conference on Algorithms, Computing and Machine Learning
  crossref 2022-08-19T19:38:41Z 置信度 0.70
• What Is Machine Learning Forensics?
  Data aggregation and preparation are tasks an investigator needs to perform before any analysis. When data is properly prepared, the investigator gains an understanding and insight into the criminals’ method of operation. This is an important concept: preparin…
  crossref 2011-07-19T21:09:43Z 置信度 0.70
• Geospatial Big Data for Machine Learning
  Geospatial Big Data for Machine Learning - 1
  crossref Bharath H. Aithal, P.S. Prakash 2022-11-11T13:32:41Z 置信度 0.70
• 70 years of machine learning in geoscience in review
  crossref 2020-09-18T09:17:48Z 置信度 0.70
• Self-Improving Reactive Agents Based on Reinforcement Learning, Planning and Teaching
  crossref Long-Ji Lin 2003-04-04T16:55:36Z 置信度 0.70
• Relational Instance-Based Learning with Lists and Terms
  crossref Tamás Horváth, Stefan Wrobel, Uta Bohnebeck 2002-12-22T05:54:50Z 置信度 0.70
• Active Learning for Vision-Based Robot Grasping
  crossref Marcos Salganicoff, Lyle H. Ungar, Ruzena Bajcsy 2003-02-06T17:07:14Z 置信度 0.70
• Agricultural modernization with forecasting stages and machine learning
  Humans watched the natural phenomena and looked at the behavior of birds and animals. The procedure called “sortilege” or “cleromancy” includes anticipating the future from sticks, beans or different things drawn indiscriminately from an assortment. The inform…
  crossref A.K. Awasthi, Arun Kumar Garov 2020-12-10T10:57:28Z 置信度 0.70
• A Study of Explanation-Based Methods for Inductive Learning
  crossref Nicholas S. Flann, Thomas G. Dietterich 2003-04-04T16:55:36Z 置信度 0.70
• Learning Nested Differences of Intersection-Closed Concept Classes
  crossref David Helmbold, Robert Sloan, Manfred K. Warmuth 2003-04-04T16:55:36Z 置信度 0.70
• Elevator Group Control Using Multiple Reinforcement Learning Agents
  crossref Robert H. Crites, Andrew G. Barto 2002-12-22T05:04:10Z 置信度 0.70
• Shallow Learning vs. Deep Learning in Engineering Applications
  crossref Fereshteh Jafari, Kamran Moradi, Qobad Shafiee 2024-10-12T18:01:28Z 置信度 0.70
• PAC Learning of One-Dimensional Patterns
  crossref Paul W. Goldberg, Sally A. Goldman, Stephen D. Scott 2003-02-06T17:08:17Z 置信度 0.70
• Numerical Solution of Machine Learning Control Problems
  crossref Askhat Diveev, Elizaveta Shmalko 2021-10-23T16:02:25Z 置信度 0.70
• Machine Learning Approach to Evolutionary Computation
  crossref Hitoshi Iba 2018-06-15T17:34:53Z 置信度 0.70
• Machine Learning with Neural Networks
  This chapter presents the theory, operation, and implementation of artificial neural networks. It starts with a basic overview of neurons and perceptrons, and then explains how perceptrons are combined into multilayer perceptrons, or MLPs. Then it discusses th…
  crossref Matthew Scarpino 2024-08-28T17:57:00Z 置信度 0.70
• Guest Editorial
  crossref Katharina Morik, Francesco Bergadano, Wray Buntine 2003-04-04T16:55:36Z 置信度 0.70
• Performance Comparison of Different Machine Learning Techniques towards Prevalence of Cardiovascular Diseases (CVDs)
  In these days, a large population worldwide has been suffering from CVDs or heart disease problems and millions of people have died due to this problem i.e. not getting proper treatment at the right time. In this way, effective automated system is required to …
  crossref Sachin Kamley 2022-03-17T17:03:25Z 置信度 0.70
• Abductive Explanation-Based Learning: A Solution to the Multiple Inconsistent Explanation Problem
  crossref William W. Cohen 2003-04-04T16:55:36Z 置信度 0.70
• Machine Learning-Based Fault Detection and Prognostics in Industrial Systems
  Machine learning (ML) has revolutionized fault detection and prognosis in industrial systems, offering unprecedented capabilities for predictive maintenance and real-time monitoring. The integration of advanced ML models, including supervised learning, deep le…
  crossref Thejo Lakshmi Gudipalli, R. Jeevitha 2026-05-13T04:03:28Z 置信度 0.70
• Linear Least-Squares Algorithms for Temporal Difference Learning
  crossref Steven J. Bradtke, Andrew G. Barto 2003-02-06T17:07:14Z 置信度 0.70
• Enhanced credit risk prediction using deep learning and SMOTE-ENN resampling
  Credit risk prediction is a vital task in financial services, ensuring that institutions can manage their lending risks effectively. This study investigates the effectiveness of deep learning (DL) models for credit risk prediction, with a focus on addressing t…
  crossref Idowu Aruleba, Yanxia Sun 2025-06-27T11:20:32Z 置信度 0.70
• Lower the barrier of machine learning : meta learning for transfer learning and autoML
  Recent years, machine learning becomes the major methodology to develop artificial intelligence applications, due to the trend of using big data in machine learning. However, traditional machine learning may have three barriers: lack of data, poor feature qual…
  crossref Wenyuan Dai 2020-07-10T00:56:11Z 置信度 0.70
• Learning Team Strategies: Soccer Case Studies
  crossref Rafał P. Sałustowicz, Marco A. Wiering, Jürgen Schmidhuber 2002-12-22T05:04:10Z 置信度 0.70
• Learning to Perceive and Act by Trial and Error
  crossref Steven D. Whitehead, Dana H. Ballard 2003-04-04T16:55:36Z 置信度 0.70
• Machine Learning
  This chapter focuses on the most popular artificial intelligence technique for infusing smarts into software—Machine Learning (ML). It presents examples of using ML to capture patterns in data and capture these patterns in artifacts called models. ML can be cl…
  crossref 2019-04-19T07:52:51Z 置信度 0.70
• Introductory Chapter: Machine Learning and Biometrics
  crossref Jucheng Yang, Yarui Chen, Chuanlei Zhang, Dong Sun Park 等 2018-08-29T12:55:28Z 置信度 0.70
• Scanning probe microscopy in the age of machine learning
  Scanning probe microscopy (SPM) has revolutionized our ability to explore the nanoscale world, enabling the imaging, manipulation, and characterization of materials at the atomic and molecular level. However, conventional SPM techniques suffer from limitations…
  crossref Md Ashiqur Rahman Laskar, Umberto Celano 2023-11-10T08:23:42Z 置信度 0.70
• A Bayesian/Information Theoretic Model of Learning to Learn via Multiple Task Sampling
  crossref Jonathan Baxter 2002-12-22T04:48:21Z 置信度 0.70
• Learning from Innate Behaviors: A Quantitative Evaluation of Neural Network Controllers
  crossref Noel E. Sharkey 2002-12-22T04:48:21Z 置信度 0.70
• Predicting intraoperative bleeding in patients undergoing a hepatectomy using multiple machine learning and deep learning techniques
  crossref Qiong Xue, Yu Zhu, Lihua Yang, Wen Duan 等 2021-06-30T21:18:21Z 置信度 0.70