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Sequential data modeling has received growing interests due to its impact on real world problems. Sequential data is ubiquitous -- financial transactions, advertise conversions and disease evolution are examples of sequential data. A long-standing challenge in sequential data modeling is how to capture the strong hidden correlations among complex features in high volumes. The sparsity and skewness...
We present a nonparametric Bayesian model for multi-task learning, with a focus on feature selection in binary classification. The model jointly identifies groups of similar tasks and selects the subset of features relevant to the tasks within each group. The model employs a Dirchlet process with a beta- Bernoulli hierarchical base measure. The posterior inference is accomplished efficiently using...
Radial basis function (RBF) neural network is used to predict the blast furnace hot metal based on its characteristics such as fast convergence and global optimization. As hot metal silicon content had close relationship with furnace temperature, the change of temperature in furnace was reflected indirectly by hot metal silicon content. Newrbe function in Matlab was applied for function approximation...
In order to overcome shortcomings of basic hidden markov model (HMM), a hybrid model of multi-layer perceptron (MLP) and continuous hidden markov model (CHMM) is presented which bases on basic HMM. In this hybrid mode, MLP calculates each state’s output probability instead of CHMM. The main purpose of this model is to improve the recognition ratio of CHMM by means of the strong of MLP’s nonlinear...
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