Adaptive resonance theory (ART) is a type of neural network model that focuses on unsupervised learning and pattern recognition, while maintaining stability and plasticity in the learning process. It emphasizes the importance of matching incoming data with existing categories, allowing the system to adapt to new information without losing previous knowledge. This balance is crucial for effective competitive learning and vector quantization, as it ensures that the model can learn from new data while preserving the integrity of learned patterns.
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