https://ogma.newcastle.edu.au/vital/access/ /manager/Index en-au 5 https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41607 smoothness, which requires that the posterior distribution should not be dominated by the data. To address this limitation we introduce the sparse and smooth Dirichlet Belief Network (ssDirBN) which can achieve both sparsity and smoothness simultaneously, thereby increasing modelling flexibility over the DirBN. This gain is achieved by introducing binary variables to indicate whether each entity’s latent distribution at each layer uses a particular component. As a result, each latent distribution may use only a subset of components in each layer, and smoothness is enforced on this subset. Extra efforts on modifying the models are also made to fix the issues which is caused by introducing these binary variables. Extensive experimental results on real-world data show significant performance improvements of ssDirBN over state-of-the-art models in terms of both enhanced model predictions and reduced model complexity.]]> Mon 08 Aug 2022 10:31:34 AEST ]]> https://ogma.newcastle.edu.au/vital/access/ /manager/Repository/uon:41530 Fri 05 Aug 2022 15:32:58 AEST ]]>