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Exact(6)

Its distinctive characteristics can modify the update rules for the two message propagations used in affinity propagation (AP).

An alternative solution to this problem, (inspired by Wilson 2001; and Cattaneo 2008 20144), would modify the update rule in such a way that those extreme priors that give extremely small likelihoods to the evidence are excised from the representor.

As the noise level σ n will be changed during iterations, we modify the update rule into a function λ = 2 γ Q σ ˜ n 2 / b, where the noise variance σ ˜ n 2 can be computed by σ ˜ n 2 = y − Ω x ^ 2 2 / L. The iterative process will be terminated when the stopping criterion is satisfied: Δ J k = J x ^ k − J x ^ k − 1 ≤ ξ, (12).

In this paper, we modify the update step in Newton-Raphson method to propose a differentially private distributed logistic regression model based on both public and private data.

Similarly, it may be possible to modify the update formula for c xy (i, j) to further improve the performance of the proposed message-passing scheme, and we are currently in the process of investigating several different implementations.

The state a d is not in general a steady state of the damaged network, and so we modify the update rules such that it becomes one, i.e. a d → a s d.

Similar(54)

Indeed, all other knockouts required modifying the update rules for 1, 2, or 3 nodes; the search algorithm identified roughly between 80 and 300 possible rule modifications for each of these nodes.

Of the steady state attractors that are not robust to damage, all (100%) were stabilized after application of the update rule modifications as described in the Methods section, i.e., by simultaneously modifying the update rules for all sensitive nodes, we unambiguously ensure that the damaged state becomes a steady state in the repaired network (that a d → a s d ).

This implementation can be easily extended to an arbitrary number of steps by modifying the update function in order to read previous probabilities from p1 during the odd iterations and from p2 during the even iterations (and writing the current probability in the other variable).

The balanced training SVM-SGD also modifies the updating rule using the skewed misclassification costs.

To see how to best modify the atom update rule, we first consider the case, where the corruption occurs always in the same locations, meaning M n =M.

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