Showing 1–50 of 27850 results

  1. Reviews | | Open

    • ...As an advanced computational approach, machine learning, which enables people to... ...of PD. In particular, machine learning models have been developed to... ...the relevant literature on using machine learning models for genetic and transcriptomic... .... Undoubtedly, the use of machine
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    • ...effective treatments for these diseases. Machine learning, a subfield of artificial... ...this Review, we discuss how machine learning can aid early diagnosis and... ...of the different applications of machine learning is the integration of multiple...
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    • Recently, deep learning has unlocked unprecedented success in... ...and speech. However, deep learning is only beneficial if the... ...UKBiobank brain images against established machine learning references. On MNIST and... ...the examined kernel and deep learning methods.
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    • ...work we introduce the novel learning setting of explanatory interactive learning and illustrate its benefits on... ...phenotyping research task. Explanatory interactive learning adds the scientist into the... ...results demonstrate that explanatory interactive learning can help to avoid Clever...
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    • .... Here, we employ a machine learning approach for predicting and designing... ...neural network structures in determining machine-learning-generated real-space structures and...
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    • ...Here, we find that a machine learning algorithm that is trained to... ...principle. Our results indicate that machine learning techniques can be used to...
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    • ..., image classification, and Ising machines. Modern machine learning paradigms, as extreme learning machines, reveal that disordered and... ...we use living matter for machine learning? Here, we employ living... ...to demonstrate a random optical learning machine (ROM) for the investigation...
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    • ...an efficient strategy by using machine learning to conduct SF6 decomposed components... ...the robust feature engineering for machine learning based GIS diagnosing model.... Six machine learning algorithms were used to establish... ...were trained by learning the collection dataset...
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  14. Reviews |

    • ...of ensemble methods and deep learning models have led to the... ...research. Traditionally, these two machine learning techniques have largely been treated... ...recent emergence of ensemble deep learning—wherein the two machine learning techniques are combined to achieve... ...learning and...
  15. Research | | Open

    • ...prediction models of FN using machine learning algorithms. Medical records of... ...risk factors for FN. Using machine learning algorithms, prediction models were... ...regression. The AUC improved by machine learning was 0.908. Machine learning improves the prediction of FN...
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    • ...alternative extraction approach by using machine learning trained on more than a... ...in each half bandgap. The machine learning model is trained to predict... ...using experimental measurements, where the machine learning predicts defect energy level and... ...successful application of machine...
  18. Reviews | | Open

    • ...toolbox in recent years is machine learning. This collection of statistical... ...works that develop and apply machine learning to solid-state systems.... ...a starting point, we introduce machine learning principles, algorithms, descriptors,... ...with the description of different machine
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    • ...performance and generalizability of traditional machine learning and deep learning models for distinguishing glioblastoma from... ...(DNN) and seven traditional machine learning classifiers combined with one of... ...to the best-performing traditional machine learning model (adaptive boosting...
  20. Research | | Open

    • We aimed to develop machine learning models to accurately predict bronchiolitis... ...as predictors, we developed four machine learning models: Lasso regression, elastic... ...the positive pressure ventilation outcome, machine learning models outperformed reference model (... ...54–0.60]). The machine
  21. Reviews | | Open

    • ...of several orders of magnitude. Machine learning experiences increased popularity as a... ...Edisonian trial-and-error approaches. Machine learning offers opportunities to gain detailed...
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    • In data-intensive science, machine learning plays a critical role in... .... However, the potential of machine learning has been limited in the... ...database and property. The multitask learning of miscellaneous factors increases the...
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  28. Reviews |

    • Reinforcement learning (RL) is a framework... ...importance to psychology, neuroscience and machine learning. Interactions between these fields,... ...to better account for human learning, and in particular its... ...an unnecessarily narrow perspective on learning and decision-making. Here,...
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  30. Reviews |

    • ...digital libraries, the field of machine vision can now be effectively... ...not have deep expertise in machine vision techniques. Although these... ...other domains. The development of machine-learning approaches for the extraction of...
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  34. Reviews |

    • Machine learning is a specific application of... ...intelligence that allows computers to learn and improve from data and... ...the field of energy storage, machine learning has recently emerged as a... ...showcase the promise of various machine learning techniques for fast and accurate... ...machine...
  35. Research | | Open

    • ...and develop regression models using machine learning algorithms to estimate KOA severity... ...selected for inclusion in the machine learning algorithm. Three WOMAC subscales... ...of 0.723 and the machine learning algorithm showed a correlation of...
  36. Research | | Open

    • ...non-dimension procedure in the learning step, and there is... ...be considered as a meshless learning step that there is no... ...were not included in the learning process and were compared to... ...ANFIS method enables us to learn flow and temperature distribution throughout...
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    • ...Critical advances based on deep-learning and machine vision over the last couple... ...systems. We then developed supervised machine learning classifiers that integrate the skeletal... ...we show that the resulting machine learning approach eliminates variation both within...
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  42. Reviews |

    • ...has boosted the application of machine learning in many fields and is... ...active-matter research as well. Machine learning techniques have already been successfully... ...to analyse time series. Yet machine learning can also help to disentangle... ...art in the application of machine learning...
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  47. Reviews |

    • ...researchers are adapting methods from machine learning (ML) to PCG problems... ...directions such as procedurally generated learning environments. Although originating in... ...critical to creating more general machine intelligence.
  48. Reviews |

    • ...we summarize recent progress in machine learning for the chemical sciences.... We outline machine-learning techniques that are suitable for...
  49. Reviews |

    • A variety of machine learning methods such as naive Bayesian... , support vector machines and more recently deep neural... ...The integrated application of such machine learning models for end-to-end...
  50. Research |

    • ...of suitable chemical descriptors and machine-learning algorithms, including deep learning, is a considerable challenge... ...we sought to apply classic machine-learning algorithms and deep-learning approaches to a panel of... ...descriptor was combined with four machine-learning and two deep-