کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
4969924 1449983 2017 10 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Diversified dictionaries for multi-instance learning
ترجمه فارسی عنوان
فرهنگ لغت گوناگون برای یادگیری چند نمونه
کلمات کلیدی
یادگیری چند نمونه، یادگیری متنوع، یادگیری فرهنگ لغت
موضوعات مرتبط
مهندسی و علوم پایه مهندسی کامپیوتر چشم انداز کامپیوتر و تشخیص الگو
چکیده انگلیسی
Multiple-instance learning (MIL) has been a popular topic in the study of pattern recognition for years due to its usefulness for such tasks as drug activity prediction and image/text classification. In a typical MIL setting, a bag contains a bag-level label and more than one instance/pattern. How to bridge instance-level representations to bag-level labels is a key step to achieve satisfactory classification accuracy results. In this paper, we present a supervised learning method, diversified dictionaries MIL, to address this problem. Our approach, on the one hand, exploits bag-level label information for training class-specific dictionaries. On the other hand, it introduces a diversity regularizer into the class-specific dictionaries to avoid ambiguity between them. To the best of our knowledge, this is the first time that the diversity prior is introduced to solve the MIL problems. Experiments conducted on several benchmark (drug activity and image/text annotation) datasets show that the proposed method compares favorably to state-of-the-art methods.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Pattern Recognition - Volume 64, April 2017, Pages 407-416
نویسندگان
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