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Production systems may vary on the expressive power of conditions in production rules. Accordingly, the pattern matching algorithm that collects production rules with matched conditions may range from the naive—trying all rules in sequence, stopping at the first match—to the optimized, in which rules are "compiled" into a network of inter-related conditions.
In computer science, the Krauss wildcard-matching algorithm is a pattern matching algorithm. Based on the wildcard syntax in common use, e.g. in the Microsoft Windows command-line interface, the algorithm provides a non-recursive mechanism for matching patterns in software applications, based on syntax simpler than that typically offered by regular expressions.
The goal of matching is to reduce bias for the estimated treatment effect in an observational-data study, by finding, for every treated unit, one (or more) non-treated unit(s) with similar observable characteristics against which the covariates are balanced out (similar to the K-nearest neighbors algorithm).
Sequential pattern mining is a topic of data mining concerned with finding statistically relevant patterns between data examples where the values are delivered in a sequence. [ 1 ] [ 2 ] It is usually presumed that the values are discrete, and thus time series mining is closely related, but usually considered a different activity.
Prediction by partial matching (PPM) is an adaptive statistical data compression technique based on context modeling and prediction. PPM models use a set of previous symbols in the uncompressed symbol stream to predict the next symbol in the stream. PPM algorithms can also be used to cluster data into predicted groupings in cluster analysis.
To do something with a value of this Tree algebraic data type, it is deconstructed using a process called pattern matching. This involves matching the data with a series of patterns. The example function depth above pattern-matches its argument with three patterns. When the function is called, it finds the first pattern that matches its ...
Data matching Data matching is used to compare two sets of collected data. The process can be performed based on algorithms or programmed loops. Trying to match sets of data against each other or comparing complex data types. Data matching is used to remove duplicate records and identify links between two data sets for marketing, security or ...
These failures of the matching law have led to the development of the "generalized matching law", which has parameters that reflect the deviations just described. This law is a power function generalization of the strict matching (Baum, 1974), and it has been found to fit a wide variety of matching data.