Neural Network Training via Cluster-Based Data Weighting

The training data is segmented into multiple clusters based on feature vectors from the hidden layer. Each cluster represents a distinct group of data with similar characteristics, allowing the training process to focus on specific segments rather than treating all data uniformly. This segmentation enables more precise recognition accuracy improvement while maintaining manageable complexity through localized training adjustments.

The patent applies local quality by assigning different weights to different clusters based on their recognition accuracy. Clusters with lower accuracy receive higher weights, directing more training attention to problematic areas. This localized quality adjustment improves overall recognition accuracy without requiring complex global modifications to the training mechanism.

The patent performs preliminary clustering of training data based on feature vectors before the actual training process. By pre-organizing data into clusters and identifying which clusters need more attention, the system prepares the training data in advance, enabling more efficient training execution without requiring complex real-time decision-making mechanisms during training.

The system implements feedback by evaluating recognition accuracy for each cluster and using this information to adjust training weights. Clusters with lower accuracy receive higher weights in subsequent training iterations, creating a feedback loop that continuously optimizes training efficiency. This feedback mechanism improves productivity without excessive complexity because it uses simple weight adjustment based on measured performance.