One of the major disadvantage of using neural networks over traditional machine learning algorithm is
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The no free lunch theorem states that uniformly averaged over all target functions the expected error is the same for any two algorithms. Nonetheless, there are other reasons for stating that there are advantages of ANN over other algorithms. For example, the ANNs show graceful degradation were you may have noisy input data or even the removal of units and the ANN still functions. Another advantage is the inherent distributed nature of ANNs which allow better implementations across a distributed environment.
From a personal perspective the advantage that I see is the flexibility in changing the encoding of the data to fit different statements of the problem in an ANN.
The disadvantage of ANNs is that it is non trivial to extract what is the process going underneath with regards to the decisions the ANN is making
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One of the major disadvantage of using neural networks over traditional machine learning algorithm is
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The no free lunch theorem states that uniformly averaged over all target functions the expected error is the same for any two algorithms. Nonetheless, there are other reasons for stating that there are advantages of ANN over other algorithms. For example, the ANNs show graceful degradation were you may have noisy input data or even the removal of units and the ANN still functions. Another advantage is the inherent distributed nature of ANNs which allow better implementations across a distributed environment.
From a personal perspective the advantage that I see is the flexibility in changing the encoding of the data to fit different statements of the problem in an ANN.
The disadvantage of ANNs is that it is non trivial to extract what is the process going underneath with regards to the decisions the ANN is making
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