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1. Machine learning: Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of statistical algorithms that can effectively generalize and thus perform tasks without explicit instructions. Recently, generative artificial neural networks have been able to ... (Study of algorithms that improve automatically through experience) [100%] 2023-12-30 [Machine learning] [Cybernetics]...
2. Machine learning: Machine learning (ML) it is the study of computer algorithms that may improve automatically via experience and the usage of data. It is considered to be a subset of artificial intelligence. [100%] 2023-12-29 [Machine learning] [Cybernetics]...
3. Machine learning: Machine learning is a set of techniques and algorithms that allow computer programs to learn simple or complex tasks by analyzing some training data (or examples of how they should behave). Some believe machine learning is the first stage in ... [100%] 2023-12-29 [Technology] [Artificial intelligence]...
4. Machine learning: Machine learning (ML) is a field devoted to understanding and building methods that let machines "learn" – that is, methods that leverage data to improve computer performance on some set of tasks. Machine learning algorithms build a model based on sample ... (Study of algorithms that improve automatically through experience) [100%] 2023-12-12 [Machine learning] [Cybernetics]...
5. Machine learning: Machine learning is a scientific discipline that is concerned with the design and development of algorithms that allow computers to evolve behaviors based on empirical data, such as from sensor data or databases. A major focus of machine learning research ... [100%] 2023-12-31 [Machine learning]
6. Machine learning: ML Machine learning is concerned with modifying the knowledge representation structures (or knowledge base) underlying a computer program such that its problem-solving capability improves (for surveys, cf. , ). (Mathematics) [100%] 2023-10-24
7. Machine learning: Machine learning methods automatically learn statistical regularities in a training data set to make accurate predictions about new data. Two definitions are: For example, a machine learning algorithm for Machine translation may be presented with several thousand examples of sentences ... [100%] 2024-01-21
8. Machine learning in bioinformatics: Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems ... [90%] 2024-01-10 [Machine learning] [Bioinformatics]...
9. Machine learning in bioinformatics: Machine learning in bioinformatics is the application of machine learning algorithms to bioinformatics, including genomics, proteomics, microarrays, systems biology, evolution, and text mining. Prior to the emergence of machine learning, bioinformatics algorithms had to be programmed by hand; for problems ... [90%] 2024-01-10 [Machine learning] [Bioinformatics]...
10. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Applications of machine learning to quantum physics) [90%] 2024-01-10 [Machine learning] [Quantum information science]...
11. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Engineering) [90%] 2024-01-09 [Machine learning] [Quantum information science]...
12. Machine learning in physics: Applying classical methods of machine learning to the study of quantum systems is the focus of an emergent area of physics research. A basic example of this is quantum state tomography, where a quantum state is learned from measurement. (Physics) [90%] 2024-05-04 [Machine learning] [Quantum information science]...
13. Quantum machine learning: Quantum machine learning is the integration of quantum algorithms within machine learning programs.Cite error: Closing missing for tag The first letter refers to whether the system under study is classical or quantum, while the second letter defines whether a ... (Engineering) [81%] 2023-09-15 [Machine learning] [Quantum information science]...
14. Quantum machine learning: Quantum machine learning is the integration of quantum algorithms within machine learning programs. The most common use of the term refers to machine learning algorithms for the analysis of classical data executed on a quantum computer, i.e. (Interdisciplinary research area at the intersection of quantum physics and machine learning) [81%] 2023-12-29 [Machine learning] [Quantum information science]...
15. Machine learning potential: Beginning in the 1990s, researchers have employed machine learning programs to construct interatomic potentials, mapping atomic structures to their potential energies. Such machine learning potentials promised to fill the gap between density functional theory, a highly-accurate but computationally-intensive ... [81%] 2023-12-29 [Machine learning] [Materials science]...
16. Logic learning machine: Logic learning machine (LLM) is a machine learning method based on the generation of intelligible rules. LLM is an efficient implementation of the Switching Neural Network (SNN) paradigm, developed by Marco Muselli, Senior Researcher at the Italian National Research Council ... (Machine learning method) [81%] 2023-12-12 [Classification algorithms] [Machine learning algorithms]...
17. Online machine learning: In computer science, online machine learning is a method of machine learning in which data becomes available in a sequential order and is used to update the best predictor for future data at each step, as opposed to batch learning ... (Method of machine learning) [81%] 2023-12-12 [Machine learning algorithms]
18. Adversarial machine learning: Adversarial machine learning is the study of the attacks on machine learning algorithms, and of the defenses against such attacks. A survey from May 2020 exposes the fact that practitioners report a dire need for better protecting machine learning systems ... (Research field that lies at the intersection of machine learning and computer security) [81%] 2023-09-15 [Machine learning] [Computer security]...
19. Automated machine learning: Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. AutoML potentially includes every stage from beginning with a raw dataset to building a machine learning model ready for deployment. (Process of automating the application of machine learning) [81%] 2023-08-22 [Machine learning]
20. Automated machine learning: Automated machine learning (AutoML) is the process of automating the tasks of applying machine learning to real-world problems. It is the combination of automation and ML. (Process of automating the application of machine learning) [81%] 2024-08-07 [Machine learning]