Training proceeds by multiple iterations of random merging in a volume. During training some node pairs may be merged that were encountered in previous iterations, resulting in duplicate entries in the training data, which could adversely affect performance.
We should try ensuring that each data entry is unique and see how classifier performance is affected.
Training proceeds by multiple iterations of random merging in a volume. During training some node pairs may be merged that were encountered in previous iterations, resulting in duplicate entries in the training data, which could adversely affect performance.
We should try ensuring that each data entry is unique and see how classifier performance is affected.