دانلود مقاله انگلیسی رایگان:یادگیری تقویتی عمیق یک شات برای طبقه بندی هوشمندانه مصنوعی در سیستم های خبره - 2020
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  • Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems
    Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems

    سال انتشار:

    2020


    عنوان انگلیسی مقاله:

    Deep reinforcement one-shot learning for artificially intelligent classification in expert aided systems


    ترجمه فارسی عنوان مقاله:

    یادگیری تقویتی عمیق یک شات برای طبقه بندی هوشمندانه مصنوعی در سیستم های خبره


    منبع:

    Sciencedirect - Elsevier - Engineering Applications of Artificial Intelligence, 91 (2020) 103589. doi:10.1016/j.engappai.2020.103589


    نویسنده:

    Anton Puzanov, Senyang Zhang, Kobi Cohen ∗


    چکیده انگلیسی:

    In recent years there has been a sharp rise in applications, in which significant events need to be classified but only a few training instances are available. These are known as cases of one-shot learning. To handle this challenging task, organizations often use human analysts to classify events under high uncertainty. Existing algorithms use a threshold-based mechanism to decide whether to classify an object automatically or send it to an analyst for deeper inspection. However, this approach leads to a significant waste of resources since it does not take the practical temporal constraints of system resources into account. By contrast, the focus in this paper is on rigorously optimizing the resource consumption in the system which applies to broad application domains, and is of a significant interest for academic research, industrial developments, as well as society and citizens benefit. The contribution of this paper is threefold. First, a novel Deep Reinforcement One-shot Learning (DeROL) framework is developed to address this challenge. The basic idea of the DeROL algorithm is to train a deep-Q network to obtain a policy which is oblivious to the unseen classes in the testing data. Then, in real-time, DeROL maps the current state of the one-shot learning process to operational actions based on the trained deep-Q network, to maximize the objective function. Second, the first open-source software for practical artificially intelligent one-shot classification systems with limited resources is developed for the benefit of researchers and developers in related fields. Third, an extensive experimental study is presented using the OMNIGLOT dataset for computer vision tasks, the UNSW-NB15 dataset for intrusion detection tasks, and the Cleveland Heart Disease Dataset for medical monitoring tasks that demonstrates the versatility and efficiency of the DeROL framework.
    Keywords: Deep reinforcement learning | One-shot learning | Network optimization | Online classification


    سطح: متوسط
    تعداد صفحات فایل pdf انگلیسی: 12
    حجم فایل: 1319 کیلوبایت

    قیمت: رایگان


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