Information Technology Reference
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1.7 Machine Learning
Knowledge, knowledge representation and knowledge based reasoning
algorithms are always considered at the heart of AI, while machine learning can
be viewed as a most critical problem. For hundreds of years, the psychologists
and philosophers held that the basic mechanism of learning is trying to transfer
successful behaviors in one practice to other similar practices. Learning is the
process of acquiring knowledge, gaining experience, improving performance,
discovering rules and adapted to environments. Fig. 1.2 illustrates a simple
model of learning with four basic elements of a learning system. The
environment provides external information, similar to a supervisor. The learning
unit processes information provided by the environment, corresponding to
various learning algorithms. The knowledge base stores knowledge in certain
knowledge representation formalisms. The performing unit accomplishes certain
tasks based on the knowledge in the knowledge base, and sends the execution
results to the learning unit through feedbacks. The system can be gradually
improved through learning. Research on machine learning not only enables
machines to automatically acquire knowledge and obtain intelligence, but also
uncovers principles and secrets of human thinking and learning, and even helps
to improve the efficiency of human learning. Research on machine learning also
has a great impact on memory storage patterns, information input methods and
computer architectures.
Knowledge
Base
Environment
Learning
Performing
Feedback
Fig. 1.2. Simple model of learning(Simon, 1983)
Research in machine learning roughly experienced four stages. The first and
initial stage is learning without knowledge, focusing on neural models and self
adaptative and self organization systems based on decision theories. However, as
neural models and decision theories were fairly restricted and only achieved
limited success, the research passion gradually depressed. The second stage in
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