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Multiple kernel learning allows for an optimal kernel function to be learned in a computationally efficient manner.
By coalescing them, it is expected to have a wider function to be learned, thus increasing the amount of feature information.
Binary classification in the context of DLs is called concept learning, as the function to be learned is expected to be characterizable by a concept.
In this framework, the learner receives samples and must select from a certain class a hypothesis that approximates the function to be learned.
It is worth pointing out that the required number of training frames has not to be very large to achieve a good performance level (in our tests we used about 30 40 training frames), by taking into account also the fact that a single frame usually contains several people clusters at different distances, so it may cover several cases of the function to be learned.
The gene expression measurements (y j i ) j = 1 J can be modelled as a function of time points (t j ) j = 1 J : y j i = g i (t j ) + ε j i Where g i is a regression function to be learned from the data and is defined as: g i (t j ) = β 0 i + ∑ q = 1 Q β q i B i q B = (B iq ) is the J × (Q + 1) design matrix specified below and β i = (β 0 i,.... β Q i ) is a vector of regression coefficients.
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The main idea of FN consists of allowing the f functions to be learned while suppressing the weights.
Despite our detailed knowledge of its structure and function, much remains to be learned about how the machinery is put together.
Next, we examined the effect of the size of the learning task (i.e. the number of Boolean functions that need to be learned) on the performance of the different methods.
To elucidate the roles that fungi play in carbon sequestration and ecosystem functioning, there remains much to be learned about their contribution to biopolymer degradation and biogeochemical cycling.
A large number of the affected genes are of unknown function, which means that much remains to be learned about the full influence of amniotic fluid on GBS.
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Since I tried Ludwig back in 2017, I have been constantly using it in both editing and translation. Ever since, I suggest it to my translators at ProSciEditing.

Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com