Showing 1–50 of 2221 results

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    • ...introduce a unique network-based machine learning platform to identify putative food... ...approved anti-cancer therapies. A machine-learning algorithm of random walks on... ...cancer-beating molecules using these ‘learned’ interactome activity profiles. The...
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    • ...study aimed to determine whether machine learning can be applied to create... ..., and IV - equivocal. Machine learning was deployed to create a... ...is the first study deploying machine learning for the automatic classification of... ...The results indicate that using machine learning enables...
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    • ...This paper proposed a Deep Learning method to predict the Blood... ...three datasets proved that Deep Learning method achieves better performance than... ...The results proved that Deep Learning methods can significantly improve the...
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    • Several machine learning approaches have been proposed for... ...end, we proposed a novel machine learning method which is based on... ...outcome. Further, the proposed machine learning method was found to achieve...
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    • As intelligent machines have become widespread in various... ...productive interactions between humans and machines. However, neurocognitive understanding of... human trust in machines is limited. In this... ...obtained during non-reciprocal human-machine interactions. Human subjects supervised...
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    • ...unknown. The recent development of machine learning (ML) approaches incited us...
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    • ...aimed to establish a novel machine-learning model for predicting hepatocellular carcinoma... ...predictive model, we developed a machine-learning framework which developed optimized classifiers... ...novel predictive model using a machine-learning approach reduced the misclassification rate...
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    • The application of machine learning to predict materials’ properties... ...an alternative route, we combine machine learning with high-throughput molecular dynamics... ...comparing the performances of select machine learning algorithms, we discuss the... ..., and interpretability in machine...
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    • We analyze how accurately supervised machine learning techniques can predict the lowest... ...the possible success of supervised machine learning tasks, namely the depth...
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    • ...on developing different types of machine learning models, including both deep... ...patient features we build the machine learning models upon include both knowledge... ...demonstrate that the complex deep learning models in this case cannot...
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    • ...comes with great challenges for machine learning algorithms that limit their use... ...challenges the scalability of most learning algorithms. Additionally, most algorithms... ...compression theory, for rule-based learning algorithms that produce highly interpretable... ...accelerate learning...
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    • ...2017, we applied a deep learning-based survival prediction method in... ...prediction using DeepSurv, a deep learning based-survival prediction algorithm,... ...added features. Thus, deep learning-based survival prediction may improve...
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    • ...was first initiated by introducing machine learning to high-throughput growth assays... ...were subjected to decision tree learning. The results showed that...
  37. Research | | Open

    • ...computation and memory, process and learn from data with limited energy... ...however, is to map existing learning algorithms onto a chip:... ...for a physical implementation, a learning rule should ideally be tolerant... ..., and local. Restricted Boltzmann Machines (RBM), for their...
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    • Reinforcement learning involves decision-making in dynamic... ...the laser-chaos-based reinforcement learning should be clarified. In... ...the way for ultrafast reinforcement learning by taking advantage of the...
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    • ...samples. In contrast, conventional machine learning methods require large amounts of... ...cognitive biases that promote fast learning. Here, we developed a... ...gap between human beings and machines in this type of inference... ...a human cognitive model into machine learning algorithms and...
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    • Machine learning is a field of computer... ...science that builds algorithms that learn. In many cases, machine learning algorithms are used to recreate... ...a source of inspiration for machine learning, little effort has been... ...brains as a guide for machine learning algorithms. Here we...
  47. Research | | Open

    • ...-infrared spectroscopy (fNIRS) and machine learning for the identification of a... ...ranking was evaluated using three learning models separately, linear discriminant... ...K-NN) and support vector machines (SVM) using the linear...
  48. Research | | Open

    • Body-machine interfaces, i.e. interfaces... ...mobility; however, how children learn these novel interfaces is poorly... ...understood. Here we characterized the learning of a body-machine interface in young unimpaired adults... ...had much greater difficulty in learning the task compared to adults...
  49. Research | | Open

    • ...study aimed to develop a machine learning-based predictive model for future... ...nationwide dataset. The gradient boosting machine (GBM) was exploited to... ...This study demonstrated that a machine learning-based predictive model might be... ...the predictive performance, such a machine
  50. Research | | Open