Determination of development priority of information system subsystems is a problem that warrants resolution during information system development. It has been proven, previously, that this problem of information syst...
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ISBN:
(纸本)9781509025435
Determination of development priority of information system subsystems is a problem that warrants resolution during information system development. It has been proven, previously, that this problem of information system development order is in fact NP-complete, NP-hard, and APX-hard. To solve this problem on a general case we have previously developed Monte-Carlo randomized algorithm, calculated complexity of this algorithm, and so on. After previous research we were able to come into possession of digraphs that represent real-world information systems. Therefore, in this paper we will empirically analyze Monte-Carlo algorithm to determine how the algorithm works on real-world examples. Also, we will critically review the results and give some possible areas of future research as well.
We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only p...
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ISBN:
(纸本)9781632660244
We propose a learning setting in which unlabeled data is free, and the cost of a label depends on its value, which is not known in advance. We study binary classification in an extreme case, where the algorithm only pays for negative labels. Our motivation are applications such as fraud detection, in which investigating an honest transaction should be avoided if possible. We term the setting auditing, and consider the auditing complexity of an algorithm: the number of negative labels the algorithm requires in order to learn a hypothesis with low relative error. We design auditing algorithms for simple hypothesis classes (thresholds and rectangles), and show that with these algorithms, the auditing complexity can be significantly lower than the active label complexity. We also show a general competitive approach for learning with outcome-dependent costs.
An algorithm is presented for approximating the arbitrary powers of a black box unitary operation, U-t, where t is a real number and U is a black box implementing an unknown unitary. The complexity of this algorithm i...
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An algorithm is presented for approximating the arbitrary powers of a black box unitary operation, U-t, where t is a real number and U is a black box implementing an unknown unitary. The complexity of this algorithm is calculated in terms of the number of calls to the black box, the errors in the approximation and a certain 'gap' parameter. For general U and large t, one should apply U a total of left perpendiculartright perpendicular times followed by our procedure for approximating the fractional power U-t-(left perpendiculartright perpendicular). An example is also given where for large integers t, this method is more efficient than direct application of t copies of U. Further applications and related algorithms are also discussed.
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