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• W1530597628 abstract "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. KeywordsLogic ProgramFinite Impulse ResponseInput StreamInductive Logic ProgrammingInput SymbolThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves." @default.
• W1530597628 created "2016-06-24" @default.
• W1530597628 creator A5012813959 @default.
• W1530597628 date "1993-01-01" @default.
• W1530597628 modified "2023-09-27" @default.
• W1530597628 title "Identifying and using patterns in sequential data" @default.
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• W1530597628 doi "https://doi.org/10.1007/3-540-57370-4_33" @default.
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