This article gives an overview of machine learning. This is an introductory article and a starting point to learn the various types of machine learning algorithms.
WHAT IS MACHINE LEARNING?
To solve problems computers require intelligence. Learning is central to intelligence. As intelligence requires knowledge, it is necessary for the computers to acquire knowledge. Machine learning serves this purpose.
Machine learning refers to a system capable of acquiring and integrating the knowledge automatically. The capability of the systems to learn from experience, training, analytical observation, and other means, results in a system that can continuously self-improve and thereby exhibit efficiency and effectiveness.
A machine learning system usually starts with some knowledge and a corresponding knowledge organization so that it can interpret, analyze, and test the knowledge acquired.
Learning System Model
The figure shown above is a typical learning system model. It consists of the following components.
1. Learning element
2. Knowledge base
3. Performance element
4. Feedback element
5. Standard system.
1. Learning element
It receives and processes the input obtained from a person ( i.e. a teacher), from reference material like magazines, journals, etc, or from the environment at large.
2. Knowledge base
This is somewhat similar to the database. Initially it may contain some basic knowledge. Thereafter it receives more knowledge which may be new and so be added as it is or it may replace the existing knowledge.
3. Performance element
It uses the updated knowledge base to perform some tasks or solves some problems and produces the corresponding output.
4. Feedback element
It is receiving the two inputs, one from learning element and one from standard (or idealized) system. This is to identify the differences between the two inputs. The feedback is used to determine what should be done in order to produce the correct output.
5. Standard system
It is a trained person or a computer program that is able to produce the correct output. In order to check whether the machine learning system has learned well, the same input is given to the standard system. The outputs of standard system and that of performance element are given as inputs to the feedback element for the comparison. Standard system is also called idealized system.
The sequence of operations described above may be repeated until the system gets the desired perfection.
There are several factors affecting the performance. They are,
• Types of training provided
• The form and extent of any initial background knowledge
• The type of feedback provided
• The learning algorithms used.
Training is the process of making the system able to learn. It may consist of randomly selected examples that include a variety of facts and details including irrelevant data. The learning techniques can be characterized as a search through a space of possible hypotheses or solutions. Background knowledge can be used to make learning more efficient by reducing the search space. The feedback may be a simple yes or no type of evaluation or it may contain useful information describing why a particular action was good or bad. If the feedback is always reliable and carries useful information, the learning process will be faster and the resultant knowledge will be correct.
The success of machine learning system also depends on the algorithms. These algorithms control the search to find and build the knowledge structures. The algorithms should extract useful information from training examples. There are several machine learning techniques available. I have explored some of the important techniques.
Rote Learning
Taking advice
Parameter adjustment
Macro-operators
Chunking
Explanation-based Learning
Clustering
Correcting Mistakes
Recording Cases
Managing Multiple Models
Back Propagation
Reinforcement Learning
Genetic Algorithms