We propose Dirichlet Process mixtures of Generalized Linear Models (DP-GLM), a new class of methods for nonparametric regression. Given a data set of input-response pairs, the DP-GLM produces a global model of the joint distribution through a mixture of local generalized linear models. DP-GLMs allow both continuous and categorical inputs, and can model the same class of responses that can be mo...

Maximum a posteriori adaptation method combines the prior knowledge with adaptation data from a new speaker, which has a nice asymptotical property, but has a slow adaptation rate for not modifying unseen models. In a strictly Bayesian approach, prior parameters are assumed known, based on common or subjective knowledge. But a practical solution is to adopt an empirical Bayesian approach, where...

The complex nature of lithium-ion battery degradation has led to many machine learning-based approaches for health forecasting being proposed in the literature. However, learning using sophisticated models can be computationally expensive, and although linear are faster they also inflexible. Piecewise-linear offer a compromise—a fast flexible alternative that is not as expensive techniques such...

We address the recognition of agent-in-place actions, which are associated with agents who perform them and places where they occur, in the context of outdoor home surveillance. We introduce a representation of the geometry and topology of scene layouts so that a network can generalize from the layouts observed in the training set to unseen layouts in the test set. This Layout-Induced Video Rep...

We propose a generative classifier which models the hidden factors that underpin the neural representation of objects with a multivariate multiple linear regression model. Our results indicate that object features derived from a feature norming study or word cooccurrences in web corpus can capture some of these hidden factors and explain a significant portion of the systematic variance in the n...