Inductive learning methods exploit a learning algorithm to explore the hypothesis space,, for identifying the hypothesis h w,x) ∈ that best approximates the true function, f, given a set of adjusted parameters w.
Those paradigms can be categorized according to (1) the class of functions they adopt as the hypothesis space,, and (2) the strategy they use to identify the hypothesis h that best mimics the target function, f.
In this regard, it is also not surprising that Augustine should have come to identify the hypothesis of the soul's voluntary descent into the body as involving both sin and cyclicism.
Because each model is associated with a biological hypothesis, model selection identifies the hypothesis that is best supported by the data.
We compared three approaches for identifying the hypotheses offering the best explanation for the simulated data.
The first was the LARS algorithm for the lasso, which was considered to have identified the correct hypothesis if the first two selected variables encoded that hypothesis.
CM and PH identified the initial hypothesis.
Furthermore, simulation showed, for reasonably large sample sizes, the LARS algorithm, even when combined with a naive P value threshold, effectively identified the correct hypotheses.
We showed that, for one example dataset, the LARS procedure identified the same hypotheses as earlier research using a structured approach.
Our conclusion is that the LARS procedure, implementing the lasso, can select hypotheses from a prespecified set, identifying the optimal hypothesis that offers the greatest consistency with the data.
