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تسجيل مستخدم جديدالخوارزمية فيتربي عبر الإنترنت وعلاقتها بالمشايات العشوائية
On-line Viterbi Algorithm and Its Relationship to Random Walks
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نشر من قبل
Tom\\'a\\v{s} Vina\\v{r}
تاريخ النشر
2007
مجال البحث
الهندسة المعلوماتية
والبحث باللغة
English
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بالنسبة لهذا البحث، نقدم الآلية فيتربي لفك تشفير النماذج المخفية الماركوفي (HMMs) بمساحة أصغر من الخطية. وتشير تحليلنا على HMMs من طورين إلى أن الذاكرة القصوى المتوقعة المستخدمة لفك سلسلة طولها $n$ مع HMM من طور $m$ يمكن أن تكون بحد أدنى من $Theta(mlog n)$، دون تبطئ كبير في مقارنة مع الآلية الفيتربي التقليدية. وتتطلب الآلية الفيتربي التقليدية $O(mn)$ من الذاكرة، والتي هي غير عملية لتحليل السلاسل الحمضية الطويلة (مثل كروموسومات الوحدة البشرية الكاملة) وللسياقات البيانات المستمرة. كما أننا نظهر أداء الآلية فيتربي الآلية على HMM بسيط لإيجاد الجينات على السلاسل الحمضية المصطبة والحقيقية.
In this paper, we introduce the on-line Viterbi algorithm for decoding hidden Markov models (HMMs) in much smaller than linear space. Our analysis on two-state HMMs suggests that the expected maximum memory used to decode sequence of length $n$ with $m$-state HMM can be as low as $Theta(mlog n)$, without a significant slow-down compared to the classical Viterbi algorithm. Classical Viterbi algorithm requires $O(mn)$ space, which is impractical for analysis of long DNA sequences (such as complete human genome chromosomes) and for continuous data streams. We also experimentally demonstrate the performance of the on-line Viterbi algorithm on a simple HMM for gene finding on both simulated and real DNA sequences.
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