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.
If spines corresponding to the synapse in question can be identified, the hypothesis predicts time-averaged [Ca2+] in those spines would be seen to increase after LTP.
We compared three approaches for identifying the hypotheses offering the best explanation for the simulated data.
From among the consistent hypotheses, referred to as the version-space (VS), the SVM identifies the best hypothesis with the maximum margin.
A bit of thought shows that this rule reliably identifies the correct hypothesis in the long run, no matter which of the two competing hypotheses is correct.
Following this principle, each record (row) of the TTD is assigned a value that identifies the corresponding hypothesis (+1, −1 or 0, respectively).
The algorithm recursively explores the SOHN network starting from its root and identifies the most specific hypotheses that apply to the query compound (first clause of the hypothesis contract) and for which the compound is in the applicability domain (second clause of the contract).
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.
