The weighted average of each classifier's prediction is combined to provide the final prediction for each observation. An example of an ensemble approach is adaboost, which combines a lot of weak learners to create a powerful learner. When classifying the observations, weak learners are produced ...
For generic machine learning loops, you should use another library (possibly, Accelerate). While we strive to present as many use cases as possible, the scripts in our examples folder are just that: examples. It is expected that they won't work out-of-the box on your specific problem and...
[SimCLR] A Simple Framework for Contrastive Learning of Visual Representations. ICML2020. Authors:Ting Chen, Simon Kornblith, Mohammad Norouzi, Geoffrey Hinton. paper code [SimCLR v2] Big Self-Supervised Models are Strong Semi-Supervised Learners. NIPS2020. Authors:Ting Chen, Simon Kornblith, Kevi...
An interesting algorithm called AdaBoost.OC combines boosting with the ECC approach and gives an algorithm that has the performance advantages of boosting and at the same time relies only on simple binary weak learners. We propose a variant of this algorithm, which we call AdaBoost.ECC, that,...
Sun et al. propose a class-based ensemble approach which can rapidly adjust to class evolution by maintaining a base learner for each class and dynamically update the base learners with new data [38]. In addition, Wang et al. propose an ensemble model that improves online bagging (OB) and...