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Adaptive Resonance Theory (ART)
The document is a comprehensive guide on Adaptive Resonance Theory (ART) and its application in neural networks and machine learning. It details the principles of ART, explaining how it enables neural networks to remain stable and plastic in response to new information. Key concepts such as the winner-takes-all mechanism, neuron output calculation, weight updates, and the importance of supervised learning for binary classification are discussed.
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The document is a comprehensive guide on Adaptive Resonance Theory (ART) and its application in neural networks and machine learning. It details the principles of ART, explaining how it enables neural networks to remain stable and plastic in response to new information. Key concepts such as the winner-takes-all mechanism, neuron output calculation, weight updates, and the importance of supervised learning for binary classification are discussed.