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Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. In machine learning, instance-based learning or memory-based learning is a family of learning algorithms that, instead of performing explicit generalization, compare new problem instances with instances seen in training, which have been stored in memory. Instance-based learning is a kind of lazy learning. It is called instance-based because it constructs hypotheses directly from the training instances themselves. This means that the hypothesis complexity can grow with the data. A simple example of an instance-based learning algorithm is the k-nearest neighbor algorithm. Daelemans and Van den Bosch describe variations of this algorithm for use in natural language processing (NLP), claiming that memory-based learning is both more psychologically realistic than other machine-learning schemes and practically effective.