1993 | ReviewPaper | Buchkapitel
Identifying and using patterns in sequential data
verfasst von : Philip Laird
Erschienen in: Algorithmic Learning Theory
Verlag: Springer Berlin Heidelberg
Enthalten in: Professional Book Archive
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Whereas basic machine learning research has mostly viewed input data as an unordered random sample from a population, researchers have also studied learning from data whose input sequence follows a regular sequence. To do so requires that we regard the input data as a stream and identify regularities in the data values as they occur. In this brief survey I review three sequential-learning problems, examine some new, and not-so-new, algorithms for learning from sequences, and give applications for these methods. The three generic problems I discuss are:Predicting sequences of discrete symbols generated by stochastic processes.Learning streams by extrapolation from a general rule.Learning to predict time series.