Identifying and Using Patterns in Sequential Data
Identifying and Using Patterns in Sequential Data
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识别和使用序列数据中的模式
DOI：
10.1007/3-540-57370-4_33
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发表时间：
1993
期刊：
影响因子：
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通讯作者：
P. Laird
中科院分区：
文献类型：
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作者：
P. Laird
虽然基础机器学习研究大多将输入数据视为来自总体的无序随机样本，但研究人员也研究了从输入序列遵循规则序列的数据中学习。要做到这一点，我们需要将输入数据视为一个流，并在数据值出现时识别它们的规律。在这个简短的调查中，我回顾了三个序列学习问题，研究了一些新的和不太新的从序列中学习的算法，并给出了这些方法的应用。我讨论的三个一般性问题是：
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.