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  2. Constant false alarm rate - Wikipedia

    en.wikipedia.org/wiki/Constant_false_alarm_rate

    However, in most fielded systems, unwanted clutter and interference sources mean that the noise level changes both spatially and temporally. In this case, a changing threshold can be used, where the threshold level is raised and lowered to maintain a constant probability of false alarm. This is known as constant false alarm rate (CFAR) detection.

  3. Detection error tradeoff - Wikipedia

    en.wikipedia.org/wiki/Detection_error_tradeoff

    The normal deviate mapping (or normal quantile function, or inverse normal cumulative distribution) is given by the probit function, so that the horizontal axis is x = probit(P fa) and the vertical is y = probit(P fr), where P fa and P fr are the false-accept and false-reject rates.

  4. Receiver operating characteristic - Wikipedia

    en.wikipedia.org/wiki/Receiver_operating...

    The true-positive rate is also known as sensitivity or probability of detection. [1] The false-positive rate is also known as the probability of false alarm [1] and equals (1 − specificity). The ROC is also known as a relative operating characteristic curve, because it is a comparison of two operating characteristics (TPR and FPR) as the ...

  5. Probability of error - Wikipedia

    en.wikipedia.org/wiki/Probability_of_error

    Download as PDF; Printable version; ... (for example regression) ... this amounts to a false negative result. [1]

  6. Majority function - Wikipedia

    en.wikipedia.org/wiki/Majority_function

    The few systems that calculate the majority function on an even number of inputs are often biased towards "0" – they produce "0" when exactly half the inputs are 0 – for example, a 4-input majority gate has a 0 output only when two or more 0's appear at its inputs. [1] In a few systems, the tie can be broken randomly. [2]

  7. Viola–Jones object detection framework - Wikipedia

    en.wikipedia.org/wiki/Viola–Jones_object...

    The Viola–Jones object detection framework is a machine learning object detection framework proposed in 2001 by Paul Viola and Michael Jones. [1] [2] It was motivated primarily by the problem of face detection, although it can be adapted to the detection of other object classes. In short, it consists of a sequence of classifiers.

  8. Fluctuation loss - Wikipedia

    en.wikipedia.org/wiki/Fluctuation_loss

    This PDF approximates an object with one large scattering surface with several other small scattering surfaces. Examples include some helicopters and propeller-driven aircraft, as the propeller/rotor provides a strong constant signal. Model III is the analog of I, considering the case where the RCS is constant through a single scan. The pdf ...

  9. False discovery rate - Wikipedia

    en.wikipedia.org/wiki/False_discovery_rate

    The false discovery rate (FDR) is then simply the following: [1] = = [], where [] is the expected value of . The goal is to keep FDR below a given threshold q . To avoid division by zero , Q {\displaystyle Q} is defined to be 0 when R = 0 {\displaystyle R=0} .

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