کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
392329 664763 2015 24 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Transductive active learning – A new semi-supervised learning approach based on iteratively refined generative models to capture structure in data
ترجمه فارسی عنوان
یادگیری فعلی فعال یک رویکرد یادگیری نیمه نظارتی مبتنی بر مدل های نسبی تکراری برای تصدی ساختن داده ها است
کلمات کلیدی
یادگیری فعال مبتنی بر استخر، مدل های به اشتراک گذاشته شده، مدل اجزاء جداگانه، مدل سازی نسبی، یادگیری نیمه نظارتی، یادگیری انتقالی
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر هوش مصنوعی
چکیده انگلیسی

Pool-based active learning is a paradigm where users (e.g., domains experts) are iteratively asked to label initially unlabeled data, e.g., to train a classifier from these data. An appropriate selection strategy has to choose unlabeled data for such user queries in an efficient and effective way (in principle, high classification performance at low labeling costs). In our transductive active learning approach we provide a completely labeled data pool (samples are either labeled by the experts or in a semi-supervised way) in each active learning cycle. Thereby, a key aspect is to explore and exploit information about structure in data. Structure in data can be detected and modeled by means of clustering algorithms or probabilistic, generative modeling techniques, for instance. Usually, this is done at the beginning of the active learning process when the data are still unlabeled. In our approach we show how a probabilistic generative model, initially parametrized with unlabeled data, can iteratively be refined and improved when during the active learning process more and more labels became available. In each cycle of the active learning process we use this generative model to label all samples not labeled by an expert so far in order to train the kind of classifier we want to train with the active learning process. Thus, this transductive learning process can be combined with any selection strategy and any kind of classifier. Here, we combine it with the 4DS selection strategy and the CMM probabilistic classifier described in previous work. For 20 publicly available benchmark data sets, we show that this new transductive learning process helps to improve pool-based active learning noticeably.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Information Sciences - Volume 293, 1 February 2015, Pages 275–298
نویسندگان
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