Results 61 to 70 of about 2,291,078 (312)
Stopping Criterion for Active Learning Based on Error Stability [PDF]
arXiv, 2021 Active learning is a framework for supervised learning to improve the predictive performance by adaptively annotating a small number of samples. To realize efficient active learning, both an acquisition function that determines the next datum and a stopping criterion that determines when to stop learning should be considered.
arxiv
ARO-The Scientific Journal of Koya University, 2023 Technology has dominated a huge part of human life. Furthermore, technology users use language continuously to express feelings and sentiments about things.
Soran S. Badawi
doaj +1 more source
Data leakage detection in machine learning code: transfer learning, active learning, or low-shot prompting? [PDF]
PeerJ Computer ScienceWith the increasing reliance on machine learning (ML) across diverse disciplines, ML code has been subject to a number of issues that impact its quality, such as lack of documentation, algorithmic biases, overfitting, lack of reproducibility, inadequate ...
Nouf Alturayeif, Jameleddine Hassine
doaj +2 more sources
Onception: Active Learning with Expert Advice for Real World Machine Translation
Computational Linguistics, 2023 Active learning can play an important role in low-resource settings (i.e., where annotated data is scarce), by selecting which instances may be more worthy to annotate.
Vânia Mendonça +3 more
doaj +1 more source
Active Learning Polynomial Threshold Functions [PDF]
arXiv, 2022 We initiate the study of active learning polynomial threshold functions (PTFs). While traditional lower bounds imply that even univariate quadratics cannot be non-trivially actively learned, we show that allowing the learner basic access to the derivatives of the underlying classifier circumvents this issue and leads to a computationally efficient ...
arxiv
Active Machine learning for formulation of precision probiotics
International Journal of Pharmaceutics, 2022 It is becoming clear that the human gut microbiome is critical to health and well-being, with increasing evidence demonstrating that dysbiosis can promote disease. Increasingly, precision probiotics are being investigated as investigational drug products for restoration of healthy microbiome balance.
McCoubrey, LE +6 more
openaire +3 more sources
Diabetes detection based on machine learning and deep learning approaches
Multim. Tools Appl., 2023 The increasing number of diabetes individuals in the globe has alarmed the medical sector to seek alternatives to improve their medical technologies.
Boon Feng Wee +4 more
semanticscholar +1 more source
Incorporating Active Learning into Machine Learning Techniques for Sensory Evaluation of Food
International Journal of Computational Intelligence Systems, 2020 The sensory evaluation of food quality using a machine learning approach provides a means of measuring the quality of food products. Thus, this type of evaluation may assist in improving the composition of foods and encouraging the development of new ...
Nhat-Vinh Lu +3 more
doaj +1 more source
A Markovian Formalism for Active Querying [PDF]
arXiv, 2023 Active learning algorithms have been an integral part of recent advances in artificial intelligence. However, the research in the field is widely varying and lacks an overall organizing leans. We outline a Markovian formalism for the field of active learning and survey the literature to demonstrate the organizing capability of our proposed formalism ...
arxiv
Automated calculation of thermal rate coefficients using ring polymer molecular dynamics and machine-learning interatomic potentials with active learning. [PDF]
Physical Chemistry, Chemical Physics - PCCP, 2018 We propose a methodology for the fully automated calculation of thermal rate coefficients of gas phase chemical reactions, which is based on combining ring polymer molecular dynamics (RPMD) and machine-learning interatomic potentials actively learning on-
I. Novikov, Y. V. Suleimanov, A. Shapeev
semanticscholar +1 more source