Classification of Learning

CLASSIFICATION OF LEARNING

The process of learning may be of various types. One can develop learning taxonomies based on the type of knowledge representation used (predicate calculus, rules, frames, scripts etc), the type of knowledge learned (game playing, problem solving) or by areas of application (medical diagnosis, engineering etc). Generally learning may be of two types like single agent learning and multi-agent learning. A general architecture of learning process is given figure .

Single Agent Learning

Over the last four decades, machine learning’s primary interest has been single agent learning. Single agent learning involves improving the performance or increasing the knowledge of a single agent. An improvement in performance or an increase in knowledge allows the agent to solve past problems with better quality or efficiency. An increase in knowledge may also allow the agent to solve new problems. An increase in performance is not necessarily due to an increase in knowledge. It may be brought about simply by rearranging the existing knowledge or utilizing it in a different manner. Single agent learning systems may be classified according to their underlying learning strategies. These strategies are classified as follows.

Rote Learning

This strategy does not require the learning system to transform or infer knowledge. It is the simplest form of learning. It requires the least amount of inference and is accomplished by simply copying the knowledge in the same form that it will be used directly into the knowledge base. It includes learning my imitation, simple memorization and learning by being performed. For example we may use this type of learning when we memorize multiplication tables. In this method we store the previous computed values, for which we do not have for recomputed them later. Also we can say rote learning is one type of existing or base learning. For example, in our childhood, we have the knowledge that “sun rises in the east”. S o in our later stage of learning we can easily memorize the thing. Hence in this context, a system may simply memorize previous solutions and recall them when confronted with the same problem. Generally access of stored value must be faster than it would be to recompute. Methods like hashing, indexing and sorting can be employed to enable this. One drawback of rote learning is it is not very effective in a rapidly changing environment. If the environment does change then we must detect and record exactly what has changed. Also this technique must not decrease the efficiency of the system. We must be able to decide whether it is worth storing the value in the first place.

Learning from Instruction

This strategy also known as learning by being told or learning by direct instruction. It requires the learning system to select and transform knowledge into a usable form and then integrate it into the existing knowledge of the system. It is a more complex form of learning. This learning technique requires more inference than rote learning. It includes learning from teachers and learning by using books, publications and other types of instructions.

Learning by Deduction

This process is accomplished through a sequence of deductive inference steps using known facts. From the known facts, new facts or relationships are logically derived. Using this strategy, the learning system derives new facts from existing information or knowledge by employing deductive inference. It requires more inferences than other techniques. The inference method used is a deductive type, which is a valid form of inference. For example we can say x is the cousin of y if we have the knowledge of x’s and y’s parents and the rules for cousin relationships. The learner draws deductive inferences from the knowledge and reformulates them in the form of useful conclusions which preserve the information content of the original data. Deductive learning includes knowledge reformulation, compilation and organizational procedures that preserve the truth of the original formulation.

Learning by Analogy

It is a process of learning a new concept or solution through the use of similar known concepts or solutions. We make frequent use of analogical learning. The first step is inductive inference, required to find a common substructure between the problem domain and one of the analogous domains stored in the learner’s existing knowledge base. This form of learning requires the learning system to transform and supplement its existing knowledge from one domain or problem area into new domain. This strategy requires more inferencing by the learning system than previous strategies. Relevant knowledge must be found in the systems existing knowledge by using induction strategies. This knowledge must then be transformed to the new problem using deductive inference. Example of learning by analogy may include the driving technique of vehicles. If we know the driving procedure of a bike, then when we will drive a car then some sort of previous learning procedures we may employ. Similarly for driving a bus or truck, we may use the procedure for driving a car.

Learning from Examples

In this process of learning it includes the learning through various interactive and innovative examples. This strategy, also called concept acquisition. It requires the learning system to induce general class or concept descriptions from examples. Since the learning system does not have prior or analogous knowledge of the concept area, the amount of inferencing is greater than both learning by deduction and analogy. For solving a newly designed problem we may use its corresponding old examples.

Learning from Observations and Discovery

Using this strategy, the learning system must either induce class descriptions from observing the environment or manipulate the environment to acquire class descriptions or concepts. This is an unsupervised learning technique. It requires the greatest amount of inferencing among all of the different forms of learning. From an existing knowledge base, some new forms of discovery of knowledge may formed. The learning discovery process is very important in the respect of constructing new knowledge base.

Learning by Induction

Inductive learning is the system that tries to induce a general rule based on observed instances. In other words, the system tries to infer an association between specific inputs and outputs. In general, the input of the program is a set of training instance where the output is a method of classifying subsequent instance. For example, the input of the program may be color of types of fruits where the output may be the types of fruits those are useful for protein. Induction method involves the learning by examples, experimentation, observation and discovery. The search spaces encountered in learning tend to be extremely large, even by the standards of search based problem solving.

This complexity of problems is cleared by choosing a problem among the different generalizations supported by any given training data. Inductive bias refers to any method a learning program uses to constrain the space of possible generalizations. A no. of inductive learning algorithms have been developed like Probably Approximately Correct (PAC), Version spaces etc. Probably Approximately Correct learning was proposed concerning the situation that cannot be deductive. Approximately correct is recognized whenever the program can get most of the problems right. In order to increase performance of the program, learning algorithms should restrict the size of hypothesis space. On the other hand, the goal of version space is to produce a description that uses only positive examples. The program in practice, produce a description of all acceptable concepts. In detail, we may conclude that there are two sets of concepts that are produced during learning. Firstly, the most specific concept describes what the target set should be. Secondly, the least specific concept describes what should not be in the target group.

Generally inductive learning is frequently used by humans. This form of learning is more powerful than the others. We use this learning when we formulate a general concept after seeing a number of instances. For example, we can say the taste of sugar is sweet if we have the knowledge about sweetness.

Learning from Advices

In this process we can learn through taking advice from others. The idea of advice taking learning was proposed in early 1958 by McCarthy. In our daily life, this learning process is quite common. Right from our parents, relatives to our teachers, when we start our educational life, we take various advices from others. All most all the initial things and all type of knowledges we acquire through the advices of others. We know the computer programs are written by programmers. When a programmer writes a computer program he or she gives many instructions to computer to follow, the same way a teacher gives his/her advice to his students. The computer follows the instructions given by the programmer. Hence, a kind of learning takes place when computer runs a particular program by taking advice from the creator of the program.

Learning by Clustering

This process is similar to the inductive learning. Clustering is a process of grouping or classifying objects on the basis of a close association or shared characteristics. The clustering process is essentially required in a learning process in which similarity patterns are found among a group of objects. The program must discover for itself the natural classes that exist for the objects, in addition to a method for classifying instances. AUTOCLASS (Cheeseman et al., 1988) is one program that accepts a number of training cases and hypothesizes a set of classes. For any given case, the program provides a set of probabilities that predict into which classes the case is likely to be fall.