Introduction to Artificial Intelligence - Old Questions

Machine learning is a branch of AI that provides computer the ability to learn and improve its learning from experience without being written rules explicitly. It focuses on prediction based on known properties learned from the training data.

Learning by Analogy

• Learning by analogy means acquiring new knowledge about an input entity by transferring it from a known similar entity.

• This technique transforms the solutions of problems in one domain to the solutions of the problems in another domain by discovering analogous states and operators in the two domains.

E.g.  Infer by analogy the hydraulics laws that are similar to Kirchhoff's laws.

Instance Based Learning

• Instance based learning is a supervised classification learning algorithm that performs operation after comparing the current instances with the previously trained instances, which have been stored in memory.
• Time complexity of Instance based learning algorithm depends upon the size of training data. Time complexity of this algorithm in worst case is O (n), where n is the number of training items to be used to classify a single new instance.
• To improve the efficiency of instance based learning approach, preprocessing phase is required. Preprocessing phase is a data structure that enables efficient usage of run time modeling of test instance.
• Advantage of using Instance based learning over others is that it has the ability to adapt to previously unseen data, which means that one can store a new instance or drop the old instance.