Empirical Approach to Machine Learning

By Plamen Parvanov Angelov, Xiaowei Gu. 2018

80 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Fair-by-design explainable models for prediction of recidivism (2019, methodology): “We call all data points associated with a prototype data clouds , because their shape is not regular (e.g., hyper-spherical, hyper-ellipsoidal, etc.) and the prototype is not necessarily the statistical and geometric mean [7].”
Towards Explainable Deep Neural Networks (xDNN) (2019, methodology): “The algorithm absorbs the new data samples one by one by assigning then to the nearest (in the feature space) prototype: In case, the following condition [13] is met: It means that x i is out of the inﬂuence area of p j .”
Novelty Detection and Learning from Extremely Weak Supervision (2019, methodology): “In this paper, we use r∗ = √ 2− 2cos(30o) same as [12]; the rationale is that two vectors for which the angle between them is less than π/6 or 30 are pointing in close/similar directions.”
Actively Semi-Supervised Deep Rule-based Classifier Applied to Adverse Driving Scenarios (2019, methodology): “Identified prototypes in the FRB layer are the peaks of the data density and typicality in the feature space [7], [9], [20], [24].”
Towards Deep Machine Reasoning: a Prototype-based Deep Neural Network with Decision Tree Inference (2020, methodology): “tability to be a prototype due to its proximity to other data samples. 3) Conditional probability layer The conditional probability can be estimated from the empirically observed data as described in [7] where it is also called typicality˝. It can be given by eq. (3). The integral of R 1 1 p(Cjx)dx=”