Titlebook: Artificial Intelligence XXXVII; 40th SGAI Internatio Max Bramer,Richard Ellis Conference proceedings 2020 Springer Nature Switzerland AG 20

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Learning Categories with Spiking Nets and Spike Timing Dependent Plasticityn be effective. The system learns with a standard spike timing dependent plasticity Hebbian learning rule. A two layer feed forward topology is used with a presentation mechanism of inputs followed by outputs a simulated ms. later to learn Iris flower and Breast Cancer Tumour Malignancy categorisers

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Developing Ensemble Methods for Detecting Anomalies in Water Level Dataetry stations can be used to produce early warning or decision supports in risky situations. However, sometimes a device in a telemetry system may not work properly and generates some errors in the data, which lead to false alarms or miss true alarms for disasters. We then developed two types of ens

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Detecting Node Behaviour Changes in Subgraphso their popularity; . look at people’s relationships, . show how computers (devices) communicate with each other and . represent the chemical bonds between atoms. Some graphs can also be dynamic in the sense that, over time, relationships change. Since the entities can, to a certain extent, manage t

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ReLEx: Regularisation for Linear Extrapolation in Neural Networks with Rectified Linear UnitsLinear Units do enable unbounded linear extrapolation by neural networks, but their extrapolation behaviour varies widely and is largely independent of the training data. Our goal is instead to continue the local linear trend at the margin of the training data. Here we introduce ReLEx, a regularisin

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https://doi.org/10.1007/978-981-97-4962-1nt challenge with RL is that it relies on a well-defined reward function to work well for complex environments and such a reward function is challenging to define. Goal-Directed RL is an alternative method that learns an intrinsic reward function with emphasis on a few explored trajectories that rev