Saklı Markov Model Karışımları için Spektral Öğrenme Spectral Learning of Mixtures of Hidden Markov Models
نویسندگان
چکیده
Özetçe —Bu çalışmada, Saklı Markov Modeli (SMM) olarak modellenen zaman serilerinin topaklandırılması için yeni bir yöntem önerilmektedir. Topaklardaki SMM parametrelerinin güncellenmesi için son yıllarda yapay öğrenme literatüründe popüler olmaya başlayan saklı değişken modelleri için spektral öğrenme yöntemleri kullanılmaktadır. Spektral yöntemler, alışılagelmiş beklenti-enbüyütme yaklaşımının aksine, saklı değişken modellerinde tek adımda parametre kestirimi yapmamızı sağlar. Bu sebeple, önerdiğimiz yöntem hesap karmaşıklığı bakımından alışılagelmiş yöntemlerle SMM-topaklandırmaya göre hesap karmaşıklığı bakımından daha ucuzdur.
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