Hierarchical Bayesian models of subtask learning.

Learning overhypotheses with hierarchical Bayesian models.

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses--overhypotheses about feature variability (e.g. the sh...

BAYESIAN SPECIAL SECTION Learning overhypotheses with hierarchical Bayesian models

Inductive learning is impossible without overhypotheses, or constraints on the hypotheses considered by the learner. Some of these overhypotheses must be innate, but we suggest that hierarchical Bayesian models can help to explain how the rest are acquired. To illustrate this claim, we develop models that acquire two kinds of overhypotheses – overhypotheses about feature variability (e.g. the s...

Bayesian Hierarchical Models 1 Running head: BAYESIAN HIERARCHICAL MODELS Bayesian Hierarchical Models

Relevant subtask learning by constrained mixture models

We introduce relevant subtask learning, a new learning problem which is a variant of multi-task learning. The goal is to build a classifier for a task-of-interest for which we have too few training samples. We additionally have “supplementary data” collected from other tasks, but it is uncertain which of these other samples are relevant, that is, which samples are classified in the same way as ...

Analysis of Hierarchical Bayesian Models for Large Space Time Data of the Housing Prices in Tehran

Housing price data is correlated to their location in different neighborhoods and their correlation is type of spatial (location). The price of housing is varius in different months, so they also have a time correlation. Spatio-temporal models are used to analyze this type of the data. An important purpose of reviewing this type of the data is to fit a suitable model for the spatial-temporal an...