Students will use one or more strategies such as (1) Random Search, (2) Meta-Learning,
(3) Adaptive Boosting, or (4) Cascade Correlation to optimize the structure and parameters of Artificial Neural Networks (ANNs) on a given benchmark problem. Such optimization is required to maximize the recognition accuracy of ANNs designed for solving biometric tasks. In practice, the optimal structure and parameters of ANNs are difficult to find because of the need for multiple experiments with the different numbers of principal components, hidden neurons, learning rates, and types of gradient algorithms.
Method and Technology
Students will attempt to optimize ANNs which were manually designed in Assignment 1. To achieve the goal, students will apply one or more optimization strategies with different
parameters.
The examples of Random Search, Meta-Learning, Adaptive Boosting as
well as MATLAB search strategies are provided in the unit tutorials. Advanced students can use Python, MATLAB, or R to optimize Deep Learning, Convolutional Networks, and/or Conventional ANNs. AI and ML technologies are developing in many ways and so the rigorous definitions are still developing.
This means that:
(i) existing textbooks are outdated and so cannot offer efficient strategies demanded by industry and academy, and
(ii) textbooks nowadays are not interactive as online tutorials provided, for example, by Google Colab. The above-listed four strategies are new cutting-edge ways to the optimization task, supported with many tutorials available online.
Example of Optimisation Solution
An example of an ANN optimization in MATLAB is presented by a conference paper
published by the Computer Science students in Springer proceedings. Other solutions will be discussed.
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