Instance-Based Learning

• store all the training data
• when a new query instance is encountered, a set of related instances are retrieved from memory and used to classify the instance
• Advantages:
• can construct a different approximation function of the target function for each distinct query instance
• significant advantage when the target function is very complex but can be described by a collection of less complex local approximations
• Disadvantages
• cost of classifying new instances can be high, so efficiently indexing training instances very important
• use all attributes in determining similar training instances...so irrelevant or redundant attributes are a problem