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1 results about "AdaBoost" patented technology

AdaBoost, short for Adaptive Boosting, is a machine learning meta-algorithm formulated by Yoav Freund and Robert Schapire, who won the 2003 Gödel Prize for their work. It can be used in conjunction with many other types of learning algorithms to improve performance. The output of the other learning algorithms ('weak learners') is combined into a weighted sum that represents the final output of the boosted classifier. AdaBoost is adaptive in the sense that subsequent weak learners are tweaked in favor of those instances misclassified by previous classifiers. AdaBoost is sensitive to noisy data and outliers. In some problems it can be less susceptible to the overfitting problem than other learning algorithms. The individual learners can be weak, but as long as the performance of each one is slightly better than random guessing, the final model can be proven to converge to a strong learner.

Ship motion multi-step real-time prediction mixing method and system based on reinforcement learning

PendingCN114528638AOvercoming the problem of unstable forecast resultsImprove learning effectGeometric CADDesign optimisation/simulationData setAlgorithm
The invention provides a reinforcement learning-based ship motion multi-step real-time prediction mixing method and system, and the method comprises the steps: obtaining original ship motion data through an attitude sensor disposed on a ship, dividing the original ship motion data into a training data set and a prediction data set, carrying out the RTWPD, and obtaining a prediction data set; decomposing a high-frequency component and a low-frequency component of the ship motion data into subsequences with a fixed number of layers; an ORELM basic prediction model is established for each sub-sequence obtained through decomposition, an AdaBoost.MRT reinforcement learning mode is introduced, iterative training is conducted continuously, and a plurality of trained ORELM models are combined together; and finally, reconstructing a prediction result of the sub-sequence to obtain a model multi-step initial prediction result, establishing an LSSVM error correction model for a multi-step prediction error, further extracting a change rule of a small part of ship motion data contained in a multi-step error sequence, and correcting to obtain a final ship motion multi-step prediction result and outputting the final ship motion multi-step prediction result. According to the invention, the stability and accuracy of ship motion attitude multi-step prediction are improved.
Owner:WUHAN UNIV

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