Research Article
GNSS/Low-Cost MEMS-INS Integration Using Variational Bayesian Adaptive Cubature Kalman Smoother and Ensemble Regularized ELM
Algorithm 2
Ensemble RELM (ERELM) based AdaBoost-R.T.
Input: Sequence of samples | where output | | Output: Final hypothesis . | (1) Initialize: | Machine number or iteration | Distribution for all | Weak learning algorithm | Maximum number of iterations | (machines) . | Threshold for demarcating correct and | Incorrect predictionst | (2) while do | (3) Call weak learner (i.e., Algorithm 1), | providing it with distribution | (4) Build the regression model: | (5) Compute absolute relative error for each | training sample as | () | (6) Compute the error rate of as follows: | () | (7) Set | (8) Update distribution as follows: | ( ) | (9) Compute contribution of to the | final result: | (10) Normalize such that . | (11) Set . | (12) end while |
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