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According to a new report to the president by the Information Security Oversight Office ISOOO) – the federal agency that provides oversight of the government's security classification system – the cost of classification for 2010 has reached over $10.17bn.
In human genetic research, exploiting information redundancy from highly correlated genes may potentially reduce the cost of classification in terms of time and money.
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For a binary class problem, the costs of classification can be given in a cost matrix (Table 4).
Four thousand families were selected at random from all the households officially registered in 42 German municipalities, preselected on the basis of region, population figures and a cost-of-living classification.
This is the cost of better classification performance.
An interesting solution uses the quantum computing to reduce the memory space and computing cost of a classification algorithm.
An analytical upper bound for the cost of imperfect classification is developed for exponentially distributed lifetime cases.
In some cases, the cost of wrong classification can be very high in a sample of a special class, such as wrongly misclassifying cancerous individuals or patients as healthy ones.
Most of standard learning algorithms have been designed for balanced data (the data with the same frequency of samples in each class), where the cost of wrong classification is the same within all classes.
The cost of correct classification in each class is 0 [C 1,1) = C 0,0) = 0].
Even at a price of $0.65/hr, the cost of 3D classification and refinement of the 80S ribosome would have been $53.64 (Author response table 1).
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