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Thinking Deeply: Neural Networks and Deep Learning
Published in Jesús Rogel-Salazar, Advanced Data Science and Analytics with Python, 2020
Now that we have the equations that enable us to backpropagate errors, we can consider some aspects of the computational implementation of the optimisation algorithm. There are different variations and one of the most common is the so-called stochastic gradient descent where the model is changed for each training example in the dataset. In this case, the data effectively becomes available to the algorithm in sequential order. This kind of methodology is sometimes called online machine learning. Although we may get a more immediate view of the performance of the model, it is a computationally intensive affair as well as being prone to be affected by noise. In stochastic gradient descent, we update the model for every training data sample.
The Technology Infrastructure to Support Augmented Intelligence
Published in Judith Hurwitz, Henry Morris, Candace Sidner, Daniel Kirsch, Augmented Intelligence, 2019
Judith Hurwitz, Henry Morris, Candace Sidner, Daniel Kirsch
In addition, complex algorithms can be automatically adjusted based on rapid changes in variables such as sensor data, time, weather data, and customer sentiment metrics. For example, inferences can be made from a machine learning model: If the weather changes quickly, a weather predicting model can predict a tornado, and a warning siren can be triggered. The improvements in accuracy are a result of the training process and automation that is part of machine learning. Online machine learning algorithms continuously refine the models by continually processing new data in near real time and training the system to adapt to changing patterns and associations in the data.
Mitigation of the spectrum sensing data falsifying attack in cognitive radio networks
Published in Cyber-Physical Systems, 2021
Rajorshi Biswas, Jie Wu, Xiaojiang Du, Yaling Yang
Online machine learning is referred to as a learning system where data are available to the system in a sequential manner. In our system, SUs keep sending sensing results of each timeslot to DFCs. Data from nearby SUs go to the DFC in a sequential manner. Let us consider that SU becomes a DFC and SUs report to . At time , the sensing result from goes to . In addition, keeps the weight and reputation of each neighbouring SU. When the sensing results from neighbouring SUs arrive at , it calculates the sensing result based on the weighted votes of the SUs’ results.