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Thus, the problem of learning network models representing causal relationships of PTMs can be formulated as learning two adjacency matrices, one representing the relationships among observed variables (PTMs), denoted as X, and the other representing the relationships between PTMs and their hidden causes, denoted as Z, as proposed by [ 30].
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These were formulated as concretely as possible.
Specially, VS can also be formulated as to learn a function f : Structure → Activity (R d → R) based on a set of training compounds with known affinities for the target.
This can be formulated as a machine learning task using learning features, which are related to the probability for a node to appear.
The issue of learning a hash function can be formulated as a task of learning l binary classifiers.
For kernels spaces, as applied in support vector machines or kernelized vector quantization, this approach can be formulated as an online learning scheme based on the differentiable kernel.
This can be formulated as a structured output learning problem a quadratic programming problem with exponentially many constraints corresponding to the possible incorrect labelings.
This can be formulated as a classification problem and addressed by learning a classifier, traditional machine learning classification methods very easily stick to local optima which can be caused by noises of data.
Supervised tensor learning can be formulated as the optimization problem of support tensor machines (STMs) [15] which is a generalization of the standard support vector machines (SVMs) from vector data to tensor data.
Firstly, by introducing the tight frame constraint into the popular dictionary learning model, the proposed tight frame learning model could be formulated as a nonconvex optimization problem which can be solved by alternatively implementing hard thresholding operation and singular value decomposition.
A question of central interest in machine learning can be formulated as a problem of function approximation.
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