instance-based learning
[/ˈɪnstəns beɪst ˈlɜrnɪŋ/]
noun
aprendizado baseado em instâncias
1. A machine learning approach where the algorithm learns directly from training examples (instances) without explicitly building a general model, storing the training data and using it to make predictions on new data.
k-nearest neighbors is a classic example of instance-based learning where predictions are made by comparing new instances to stored training examples.
O algoritmo k-vizinhos mais próximos é um exemplo clássico de aprendizado baseado em instâncias, onde as predições são feitas comparando novas instâncias com exemplos armazenados.
2. A learning paradigm that contrasts with model-based learning, as it requires storing actual training instances rather than learning a compressed representation or explicit model.
Instance-based learning methods like case-based reasoning are useful when you have sufficient memory and computational resources to compare new problems against all stored cases.
Métodos de aprendizado baseado em instâncias, como raciocínio baseado em casos, são úteis quando você tem memória e recursos computacionais suficientes para comparar novos problemas com todos os casos armazenados.
This is primarily a technical term used in computer science and machine learning education in both Brazil and the USA. It is domain-specific jargon with consistent usage across English and Portuguese-speaking academic communities. No significant cultural variations exist in its usage.
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