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In this context, the multi-criterion function aims to maximize the first goal and minimize the second.
Secondly, we target to find a unimodular matrix to minimize the second largest PEP, which is also crucial to the BER performance, while keeping the largest PEP unchanged.
To minimize the second part, the total area of the locations with high probabilities should be small at a given time because of the sparseness of criminal incidents over the whole area S. Both of the two criteria are important.
Since the second constraint is only related to T ab, and the last constraint is only related to T ba, the joint optimization problem can be decoupled into two subproblems, i.e., optimizing T ab to minimize the first term in objective function with the constraint T ab ≥ Tmin1, and optimizing T ba to minimize the second term in objective function with the constraint T ba ≥ Tmin2.
To minimize the second type of noise, we have constrained some of the analyses even further, to individuals that have been seen at least 30 times.
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In addition, constraints are imposed to minimize the second-order derivative of the slip, with respect to both time and space (Nozu 2007).
Specifically at each iteration I, the strong classifier is updated as where the weak classifier is selected in order to minimize the second-order approximation of the cost function in (9) as follows: (10).
Then, we can further minimize the third largest PEP, the forth one, and so on.
A burn-in procedure is required to minimize the first effect.
To minimize the first part, the probability model should predict high probabilities for the locations where incidents actually happen.
Using the gradient descent method, we can minimize the fourth-order e 4(n) and the second-order e 2(n) errors for the overall filter.
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com