Pattern-matching is a means to compare the theoretically based predictions with the data in the site: "For case study analysis, one of the most preferred techniques is to use a pattern-matching logic.
Context based prediction.
Polynomial model based prediction method.
Support vector machines [47], pattern mining [48,49], and dictionary-based prediction [9] were suggested for spectrum prediction and user activity modelling.
Such models examine several statistical techniques with a major literature focus on Markov processes [10,45], Poisson processes [12,13], Bayesian models [9,14], neural networks [5,11,46], linear regression [15,16], space vector machine [47], pattern mining [48,49], and dictionary-based prediction [9].
By learning from the data, I would hopefully one day be able to determine they have a 50percentt chance of something happening in the next three weeks based on individual, pattern-based predictions.
Furthermore, the prediction of user mobility and traffic patterns based on previous data is more accurate and efficient.
This motivated a hybrid approach in which predictions from the two methods are combined by taking the top two predictions from LigsiteCSC and the top three pattern-based predictions (on average the third best pattern-based prediction is better than third best prediction from LigsiteCSC).
In several cases, addition-pattern predictions based on F NMR spectra and DFT calculations were later confirmed by X-ray crystallography.
In addition, we compared measured POPs concentrations and observed APC patterns to predictions based on the time-variant CoZMoMAN model (Breivik et al. 2010).
In [ 29], a pattern matching based pipeline for efficient rnpB gene prediction has been proposed.
