How supervised and unsupervised hebbian network different from each other?
Answers
Suppose you are providing solution to your kids for each and every situation in their life, it is called your kids are supervised. But, if your kids take their decisions out of their own understanding, it is called your kids are unsupervised.
Machine Learning: Supervised vs. Unsupervised
In machine learning, such solutions are called target or output and situations are called input or unleveled data. Situation and solution in combination it is called leveled data.
Supervised: So, if you are training your machine learning task for every input with corresponding target, it is called supervised learning, which will be able to provide target for any new input after sufficient training. Your learning algorithm seeks a function from inputs to the respective targets. If the targets are expressed in some classes, it is called classification problem. Alternatively, if the target space is continuous, it is called regression problem.
Unsupervised: Contrary, if you are training your machine learning task only with a set of inputs, it is called unsupervised learning, which will be able to find
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