79 resultados para universal scaling
em Cambridge University Engineering Department Publications Database
Resumo:
This paper compares parallel and distributed implementations of an iterative, Gibbs sampling, machine learning algorithm. Distributed implementations run under Hadoop on facility computing clouds. The probabilistic model under study is the infinite HMM [1], in which parameters are learnt using an instance blocked Gibbs sampling, with a step consisting of a dynamic program. We apply this model to learn part-of-speech tags from newswire text in an unsupervised fashion. However our focus here is on runtime performance, as opposed to NLP-relevant scores, embodied by iteration duration, ease of development, deployment and debugging. © 2010 IEEE.
Proceedings of the 5th Cambridge Workshop on Universal Access and Assistive Technology (CWUAAT 2010)
Proceedings of the 4th Cambridge Workshop on Universal Access and Assistive Technology (CWUAAT 2008)
Proceedings of the 3rd Cambridge Workshop on Universal Access and Assistive Technology (CWUAAT 2006)
Proceedings of the 3rd Cambridge Workshop on Universal Access and Assistive Technology (CWUAAT 2006)