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Field sensitivity (also known as structure sensitivity): An analysis can either treat each field of a struct or object separately, or merge them. Array sensitivity: An array-sensitive pointer analysis models each index in an array separately. Other choices include modelling just the first entry separately and the rest together, or merging all ...
It searches for sudden changes over a low frequency signal of any nature covered by heavy noise. KZA shows very high sensitivity for break detection, even with a very low signal-to-noise ratio; the accuracy of the detection of the time of the break is also very high. The KZA algorithm can be applied to restore noisy two-dimensional images.
Variance-based sensitivity analysis (often referred to as the Sobol’ method or Sobol’ indices, after Ilya M. Sobol’) is a form of global sensitivity analysis. [1] [2] Working within a probabilistic framework, it decomposes the variance of the output of the model or system into fractions which can be attributed to inputs or sets of inputs.
In numerical analysis, the ITP method (Interpolate Truncate and Project method) is the first root-finding algorithm that achieves the superlinear convergence of the secant method [1] while retaining the optimal [2] worst-case performance of the bisection method. [3]
In applied statistics, the Morris method for global sensitivity analysis is a so-called one-factor-at-a-time method, meaning that in each run only one input parameter is given a new value. It facilitates a global sensitivity analysis by making a number r {\displaystyle r} of local changes at different points x ( 1 → r ) {\displaystyle x(1 ...
Troponin I is a biomarker that responds to treatment interventions. Reductions in troponin I levels proved to reduce the risk of future CVD. [23] [24] [25] High sensitive troponin I used as a screening tool to assess a person's cardiovascular risk and has the potential to reduce the growing cost burden of the healthcare system. [26]
Relief is an algorithm developed by Kira and Rendell in 1992 that takes a filter-method approach to feature selection that is notably sensitive to feature interactions. [1] [2] It was originally designed for application to binary classification problems with discrete or numerical features. Relief calculates a feature score for each feature ...
In computer science, locality-sensitive hashing (LSH) is a fuzzy hashing technique that hashes similar input items into the same "buckets" with high probability. [1] ( The number of buckets is much smaller than the universe of possible input items.) [1] Since similar items end up in the same buckets, this technique can be used for data clustering and nearest neighbor search.