For example, if we are shown an object and we want to know if it is a chair, then we compare the description of this new object with descriptions of `typical' chairs that we have encountered before. If the description of the new object is `close' to the description of one of the stored instances then we may call it a chair. Obviously, we must defined what we mean by `typical' and `close'.
To better understand the issues involved in learning prototypes, we will briefly describe three experiments in Instance-based learning (IBL) by Aha, Kibler and Albert (1991). IBL learns to classify objects by being shown examples of objects, described by an attribute/value list, along with the class to which each example belongs.
This simple scheme works well, and is tolerant to some noise in the data. Its major disadvantage is that it requires a large amount of storage capacity.
While this scheme reduced storage dramatically, it was less noise-tolerant than the first.
Figure 1. The extension of an IBL concept is shown in solid lines. The dashed lines represent the target concept. A sample of positive and negative examples is shown. Adapted from Aha, Kibler and Albert (1991).
Learning by remembering typical examples of a concept has several other advantages. If an efficient indexing mechanism can be devised to find near matches, this representation can be very fast as a classifier since it reduces to a table look up. It does not require any sophisticated reasoning system and is very flexible. As we shall see later, representations that rely on abstractions of concepts can run into trouble with what appear to be simple concepts. For example, an abstract representation of a chair may consist of a description of the number legs, the height, etc. However, exceptions abound since anything that can be sat on can be thought of as a chair. Thus, abstractions must often be augmented by lists of exceptions. Instance-based representation does not suffer from this problem since it only consists exceptions and is designed to handle them efficiently.
One of the major disadvantages of this style of representation is that it is necessary to define a similarity metric for objects in the universe. This can often be difficult to do when the objects are quite complex.
Another disadvantage is that the representation is not human readable. What does a collection of typical instances tell us about the concept that has been learned?
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