Ensemble Learning is a machine learning technique that involves combining the predictions of multiple individual models to improve overall performance. The idea behind ensemble learning is that by aggregating the predictions of multiple models, the resulting ensemble model can often achieve better accuracy and generalization than any individual model.
An ensemble learning algorithm is applied to enhance filtering tolerance of cascaded WSSs in open ROADM environment to demonstrate ~0.8dB Q-factor improvement over MLSE after transmitting over 3200km with 16 ROADMs.
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