What are the different types of algorithms used by learning models to prevent errors?
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Various algorithms are utilized by learning models to prevent errors in different domains. In the realm of software defect prediction, machine learning algorithms play a crucial role. These algorithms include supervised learning, unsupervised learning, and computational intelligence techniques, which are employed to detect defects early in the software development life cycle, reducing effort, time, and costs significantly. Additionally, in the context of quantum kernel machine learning, error-mitigated algorithms are employed to predict labels accurately on noisy intermediate scale quantum (NISQ) devices. Specifically, quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM) algorithms have shown improved predictive performances when errors are mitigated, making them comparable to noiseless quantum simulators and classical counterparts.
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| Papers (4) | Insight |
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| The learning model in the paper uses a hybrid neural network with a linear recurrent neural network and a multilayer BP network, employing gradient descent and error backpropagation algorithms. | |
03 Jun 2022 | Error mitigation in quantum kernel machine learning models is achieved through quantum kernel Gaussian Process (qkGP) and quantum kernel Support Vector Machine (qkSVM) algorithms, enhancing predictive performance on NISQ devices. |
23 Mar 2022 2 Citations | Various Machine Learning Algorithms like Supervised Learning, Unsupervised Learning, and Computational Intelligence are utilized for Software Defect Prediction to prevent errors in the software development life cycle. |
23 Mar 2022 | Various Machine Learning Algorithms like Supervised Learning, Unsupervised Learning, and Computational Intelligence are utilized for Software Defect Prediction to prevent errors in the software development life cycle. |
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