High Dimensional Classification with Bayesian Neural Networks and Dirichlet Diffusion Trees

By Radford M. Neal, Jianguo Zhang. 2006

41 citations recorded by the source corpus.

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Deep learning in neural networks: An overview (2014, methodology): “A Bayes NN (Neal, 2006) based on an ensemble (Breiman, 1996; Dietterich, 2000a; Hashem & Schmeiser, 1992; Schapire, 1990; Ueda, 2000; Wolpert, 1992) of NNs won the NIPS 2003 Feature Selection Challenge with secret test set (Neal & Zhang, 2006).”
Rethinking Parameter Counting in Deep Models: Effective Dimensionality Revisited (2020, methodology): “However, Neal & Zhang (2006) give a straightforward argument using the invariance of the likelihood of GLMs to orthogonal linear transformation in order to justify the usage of PCA as a feature selection step.”
Classification with Bayesian Neural Networks (2005, methodology): “In entries to an earlier competition focused on feature selection (Neal and Zhang 2006), I dealt with larger numbers of inputs (up to 100,000) by either selecting a smaller number (up to about a thousand), or by reducing dimensionality using Principal Components Analysis.”
Multiscale Wavelets on Trees, Graphs and High Dimensional Data: Theory and Applications to Semi Supervised Learning (2010, methodology): “We remark that (Herbster et al., 2009; Kemp et al., 2004; Neal & Zhang, 2006) also suggested SSL algorithms using trees, albeit by different approaches.”