A máquina de comitê is a type of aprendizado em conjunto model commonly usada em aprendizado de máquina and inteligência artificial. The fundamental idea behind a committee machine is to combine the predictions of multiple independent models, typically redes neurais, to improve overall performance and robustness.
In a committee machine, each individual model, often referred to as a ‘member’ of the committee, is trained on the same task but may use different subsets of dados de treinamento or different initial conditions. This diversity among the models helps capture various aspects of the data and allows the committee to make more informed predictions. Once the models are trained, their outputs are combined—usually by averaging or voting—to produce a final prediction.
Uma das principais vantagens das máquinas de comitê é sua capacidade de reduzir overfitting, which occurs when a model learns too much from the training data and performs poorly on unseen data. By leveraging the strengths of multiple models, committee machines can provide more generalized predictions that are less sensitive to noise or outliers in the training set.
Máquinas de comitê podem ser aplicadas em diversos campos, incluindo visão computacional, processamento de linguagem natural, and predictive analytics, where improving accuracy is critical. Some popular forms of committee machines include bagging, boosting, and stacking, each of which uses different techniques for model combination and training.