کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
6863520 | 677661 | 2012 | 16 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
A life-long learning vector quantization approach for interactive learning of multiple categories
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
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چکیده انگلیسی
We present a new method capable of learning multiple categories in an interactive and life-long learning fashion to approach the “stability-plasticity dilemma”. The problem of incremental learning of multiple categories is still largely unsolved. This is especially true for the domain of cognitive robotics, requiring real-time and interactive learning. To achieve the life-long learning ability for a cognitive system, we propose a new learning vector quantization approach combined with a category-specific feature selection method to allow several metrical “views” on the representation space of each individual vector quantization node. These category-specific features are incrementally collected during the learning process, so that a balance between the correction of wrong representations and the stability of acquired knowledge is achieved. We demonstrate our approach for a difficult visual categorization task, where the learning is applied for several complex-shaped objects rotated in depth.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neural Networks - Volume 28, April 2012, Pages 90-105
Journal: Neural Networks - Volume 28, April 2012, Pages 90-105
نویسندگان
Stephan Kirstein, Heiko Wersing, Horst-Michael Gross, Edgar Körner,