Abstract
AmTriangle is a meta-dataset for machine learning (ML) that supports convenient and configurable dataset production in addition to a classifier that performs supervised learning. The idea is to have a never-ending source of supervised datasets, minimizing learning barriers to ML, for training models, and facilitating educational experiments and comparisons between different workflows. It is a “meta-dataset” because it is a solution to generate sets of samples (datasets), as many as desired, correctly classified. Each sample is a triangle, classified as “acute”, “obtuse” or “right”, according to trigonometry. Each generated dataset can be used to “teach-by-example” how to classify new samples, by different techniques, namely K-Nearest-Neighbors and neural networks. The triangles could be other analogous objects, other tuples.
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Marques, A., Amorim Silva, R.d., Madeira, F. (2023). The AmTriangle Meta-Dataset for Playing with Machine Learning. In: Mesquita, A., Abreu, A., Carvalho, J.V., de Mello, C.H.P. (eds) Perspectives and Trends in Education and Technology . Smart Innovation, Systems and Technologies, vol 320. Springer, Singapore. https://doi.org/10.1007/978-981-19-6585-2_22
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