If you want to show a sequential mode, you can describe the gradient descent algorithm. I think the question doesn't ask you to show two ways (batch and sequential) to estimate the parameters of the model, but instead to explain-either in a batch or sequential mode-how such an estimation would work.įor instance, if you are trying to estimate parameters for a linear regression model, you could just describe likelihood maximization, which is equivalent to minimize the least square error: Stochastic gradient descent BP (SGBP) is one of the main variants of BP for sequential learning applications.īasically, gradient descent is theorized in a batch way, but in practice you use iterative methods. Their basic idea was to perform experiments sequentially, minimizing the volume of the confidence region formed by the estimated para- meters in the parameter space. It is to be noted that BP is basically a batch learning algorithm. Box and Lucas (1959) and Box and Hunter (1965) pioneered the formal develop- ment of optimal strategies for parameter estimation of mechanistic models. The back-propagation (BP) algorithm and its variants have been the backbone for training SLFNs with additive hidden nodes. There are many industrial applications where online sequential learning algorithms are preferred over batch learning algorithms as sequential learning algorithms do not require retraining whenever a new data is received. Also, whenever a new data is received batch learning uses the past data together with the new data and performs a retraining, thus consuming a lot of time. In most applications, this may take several minutes to several hours and further the learning parameters (i.e., learning rate, number of learning epochs, stopping criteria, and other predefined parameters) must be properly chosen to ensure convergence. " A Fast and Accurate Online Sequential Learning Algorithm for Feedforward Networks":īatch learning is usually a time consuming affair as it may involve many iterations through the training data. The estimation methods are consistent with corresponding hypothesis tests and the confidence interval at the final analysis provides exact coverage of the unknown treatment effect.
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