Instance-based learning (IBL)

The IBL technique approaches learning by simply storing the provided training data and using it as a reference for predicting/determining the behavior of a new query. As learned in Chapter 1, Introduction to Machine learning, instances are nothing but subsets of datasets. The instance-based learning model works on an identified instance or groups of instances that are critical to the problem. The results across instances are compared and can include an instance of new data as well. This comparison uses a particular similarity measure to find the best match and predict. Since it uses historical data stored in memory, this learning technique is also called memory-based or case-based learning. Here, the focus is on the representation of the instances and similarity measures for comparison between them.

Every time a new query instance is received for processing, a set of similar, related instances are retrieved from memory, and then this data is used to classify...