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We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
We present a general method for maintaining estimates of the distribution of parameters in arbitrary models. This is then applied to the estimation of probability distribution over actions in value-based reinforcement learning. While this approach is simil ...
We apply boosting techniques to the problem of word error rate minimisation in speech recognition. This is achieved through a new definition of sample error for boosting and a training procedure for hidden Markov models. For this purpose we define a sample ...
The drainage density of a network is conventionally defined as (proportional to) the ratio of its total channelized length divided by the watershed area, and in practice, it is defined by the statistical distribution and correlation structure of the length ...
[1] The long-term morphological evolution of tidal landforms in response to physical and ecological forcings is a subject of great theoretical and practical importance. Toward the goal of a comprehensive theoretical framework suitable for large-scale, long ...
In this thesis a previously developed framework for modelling diversity of approximately periodic time series is considered. In this framework the diversity is modelled deterministically, exploiting the irregularity of chaos. This is an alternative to othe ...
In this paper, the design of probabilistic observers for mass-balance based bio- process models is investigated. It is assumed that the probability density function of every uncertain parameter, input and/or initial state is known a priori. Then, the proba ...
In this paper, the design of probabilistic observers for mass-balance based bioprocess models is investigated. It is assumed that the probability density of every uncertain parameter, input and/or initial state is known a priori. Then, the probability dens ...
A methodology towards person clustering in meeting databases is presented in this report. Such goal is generic to a number of problem in computer vision and more specifically in content-based video indexing and retrieval. First, the audio-stream was consid ...
