Differentiate between Supervised Learning and Unsupervised Learning
2286 Views • Posted On Aug. 21, 2020
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Supervised Learning |
Unsupervised Learning |
1. | Supervised Learning is also known as associative learning, in which the network is trained by providing it with input and matching output patterns. | Unsupervised Learning is also known as self-organization, in which an output unit is trained to respond to clusters of patterns within the input. |
2. | Supervised training requires the pairing of each input vector with a target vector representing the desired output. | Unsupervised training is employed in self-organizing neural networks. |
3. | During the training session, an input vector is applied to the network, and it results in an output vector. This response is then compared with the target response. | During training, the neural network receives input patterns and organizes these patterns into categories. When a new input pattern is applied, the neural network provides an output response indicating the class to which the input patterns belong. |
4. | If the actual response differs from the target response, the network will generate an error signal. | If a class cannot be found for the input pattern, a new class is generated. |
5. | The error minimization in this kind of training requires a supervisor or teacher. These input-output pairs can be provided by an external teacher, or by the system which contains a neural network. | Unsupervised training does not require a teacher, it requires certain guidelines to form groups. Grouping can be done based on color, shape, and any other property of the object. |
6. | Supervised training methods are used to perform non-linear mapping in pattern classification networks, pattern association networks, and multi-layer neural networks. | Unsupervised learning is useful for data compression and clustering. |
7. | Supervised learning generates a global model and a local model. | In this, a system is supposed to discover statistically silent features of the input population. |
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