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Learning algorithm

Reinforcement learning algorithms aim to determine the ideal behavior within a specific context based on simple reward feedback on their actions; the self-driving car is a typical example.

Supervised learning algorithms are trained on prelabeled data referred to as the training set 1).

This training process is an iterative process in which machine learning (ML) algorithms try to find the optimal combination of variables and weights given to the input variables (referred to as features) of the model with the goal of minimizing the training error as judged by the difference between predicted outcome and actual outcome 2).

Azimi P, Mohammadi HR, Benzel EC, Shahzadi S, Azhari S, Montazeri A. Artificial neural networks in neurosurgery. J Neurol Neurosurg Psychiatry . 2015;86(3):251-256.
Deo RC. Machine learning in medicine. Circulation . 2015;132(20):1920-1930.
learning_algorithm.txt · Last modified: 2018/07/21 12:30 by administrador