Discover the key differences between artificial neural networks (ANN) and unsupervised neural networks. Learn about examples like Self-Organizing Feature Maps and their applications.
Table of Contents
Question
As compare to ANN, which of the following is an example of unsupervised neural network?
A. Back-propagation network
B. Hebb network
C. Associative memory network
D. Self-organizing feature map
Answer
D. Self-organizing feature map
Explanation
A. Back-propagation network
Back-propagation networks are primarily associated with supervised learning. They utilize labeled data to adjust weights through a process that minimizes the error between predicted and actual outputs. This method is not applicable for unsupervised learning, where no labels are provided.
B. Hebb network
Hebb networks are based on Hebbian learning principles, which state that connections between neurons strengthen when they fire together. While this concept can be seen as a form of unsupervised learning, it does not fit the definition as neatly as other models, particularly in terms of clustering or mapping data without supervision.
C. Associative memory network
Associative memory networks, such as Hopfield networks, are designed for recalling patterns based on partial inputs. They can operate in both supervised and unsupervised contexts but primarily serve as a form of memory rather than clustering or dimensionality reduction.
D. Self-organizing feature map (SOM)
Self-organizing maps (SOMs), introduced by Teuvo Kohonen, are a classic example of unsupervised neural networks. They organize input data into a lower-dimensional representation while preserving the topological properties of the original data space. SOMs use competitive learning rather than error correction, allowing them to cluster similar input patterns without pre-existing labels.
Conclusion
In summary, among the options provided, the Self-organizing feature map (D) stands out as the quintessential example of an unsupervised neural network due to its ability to learn from unlabelled data by clustering similar inputs together, making it distinct from the other listed types of networks which involve supervision or specific memory functions.
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