کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
9653600 | 679206 | 2005 | 29 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Unsupervised recursive sequence processing
دانلود مقاله + سفارش ترجمه
دانلود مقاله ISI انگلیسی
رایگان برای ایرانیان
کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
مهندسی کامپیوتر
هوش مصنوعی
پیش نمایش صفحه اول مقاله
![عکس صفحه اول مقاله: Unsupervised recursive sequence processing Unsupervised recursive sequence processing](/preview/png/9653600.png)
چکیده انگلیسی
The self-organizing map (SOM) is a valuable tool for data visualization and data mining for potentially high-dimensional data of an a priori fixed dimensionality. We investigate SOMs for sequences and propose the SOM-S architecture for sequential data. Sequences of potentially infinite length are recursively processed by integrating the currently presented item and the recent map activation, as proposed in the SOMSD presented in (IEEE Trans. Neural Networks 14(3) (2003) 491). We combine that approach with the hyperbolic neighborhood of Ritter (Proceedings of PKDD-01, Springer, Berlin, 2001, pp. 338-349), in order to account for the representation of possibly exponentially increasing sequence diversification over time. Discrete and real-valued sequences can be processed efficiently with this method, as we will show in experiments. Temporal dependencies can be reliably extracted from a trained SOM. U-matrix methods, adapted to sequence processing SOMs, allow the detection of clusters also for real-valued sequence elements.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Neurocomputing - Volume 63, January 2005, Pages 69-97
Journal: Neurocomputing - Volume 63, January 2005, Pages 69-97
نویسندگان
Marc Strickert, Barbara Hammer, Sebastian Blohm,