Design and Development Input datas Machine-learning Natural structures Neural network model Unsupervised neural networks
Issue Date:
2009
Publisher:
Proceedings - 2009 1st Asian Conference on Intelligent Information and Database Systems, ACIIDS 2009
Citation:
Art. No.: 5176011, Page 307-312
Abstract:
Machine learning is the field that is dedicated to the design and development of algorithms and
techniques that allow computers to "learn". Two common types of learning that are often mentioned are
supervised learning and unsupervised learning. One often understands that in supervised learning, the
system is given the desired output, and it is required to produce the correct output for the given input, while
in unsupervised learning the system is given only the input and the objective is to find the natural structure
inherent in the input data. We, however, suggest that even with unsupervised learning, the information
inside the input, the structure of the input, and the sequence that the input is given to the system actually
make the learning "supervised" in some way. Therefore, we recommend that in order to make the machine
learn, even in a "supervised" manner, we should use an "unsupervised learning" model together with an
appropriate way of presenting the input. We propose in this paper a simple plasticity neural network model
that has the ability of storing information as well as storing the association between a pair of inputs. We then
introduce two simple unsupervised learning rules and a framework to supervise our neural network. ?? 2009
IEEE.
