4 research outputs found
Efficient and Effective Learning of HMMs Based on Identification of Hidden States
The predominant learning algorithm for Hidden Markov Models (HMMs) is local search heuristics, of which the Baum-Welch (BW) algorithm is mostly used. It is an iterative learning procedure starting with a predefined size of state spaces and randomly chosen initial parameters. However, wrongly chosen initial parameters may cause the risk of falling into a local optimum and a low convergence speed. To overcome these drawbacks, we propose to use a more suitable model initialization approach, a Segmentation-Clustering and Transient analysis (SCT) framework, to estimate the number of states and model parameters directly from the input data. Based on an analysis of the information flow through HMMs, we demystify the structure of models and show that high-impact states are directly identifiable from the properties of observation sequences. States having a high impact on the log-likelihood make HMMs highly specific. Experimental results show that even though the identification accuracy drops to 87.9% when random models are considered, the SCT method is around 50 to 260 times faster than the BW algorithm with 100% correct identification for highly specific models whose specificity is greater than 0.06
Proceedings of the 7th Sound and Music Computing Conference
Proceedings of the SMC2010 - 7th Sound and Music Computing Conference, July 21st - July 24th 2010
Anales del XIII Congreso Argentino de Ciencias de la Computaci贸n (CACIC)
Contenido:
Arquitecturas de computadoras
Sistemas embebidos
Arquitecturas orientadas a servicios (SOA)
Redes de comunicaciones
Redes heterog茅neas
Redes de Avanzada
Redes inal谩mbricas
Redes m贸viles
Redes activas
Administraci贸n y monitoreo de redes y servicios
Calidad de Servicio (QoS, SLAs)
Seguridad inform谩tica y autenticaci贸n, privacidad
Infraestructura para firma digital y certificados digitales
An谩lisis y detecci贸n de vulnerabilidades
Sistemas operativos
Sistemas P2P
Middleware
Infraestructura para grid
Servicios de integraci贸n (Web Services o .Net)Red de Universidades con Carreras en Inform谩tica (RedUNCI
CACIC 2015 : XXI Congreso Argentino de Ciencias de la Computaci贸n. Libro de actas
Actas del XXI Congreso Argentino de Ciencias de la Computaci贸n (CACIC 2015), realizado en Sede UNNOBA Jun铆n, del 5 al 9 de octubre de 2015.Red de Universidades con Carreras en Inform谩tica (RedUNCI