Although usually classifier error is the main concern in publications, in real applications classifier evaluation complexity may play a large role as well.
Although usually classifier error is the main concern in publications, in real applications classifier evaluation com- plexity may play a large role as well ...
The complexity of a certain classifier is measured as the number of floating point operations (flops) [RIDDER ET AL. 2002] it needs to classify one pattern ...
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Jan 6, 2017 · The training error does not increase with the model complexity. As the complexity increases, the model fits the data better and thus becomes ...
In the paper, a simple economic model is proposed with which a trade-off between classifier error and calculated evaluation complexity can be formulated. This ...
Classification accuracy is measured by mean error rate and mean rank of ... Journal of. Environmental Economics and Management, 5:81–102, 1978. 21. T ...
Abstract. We discuss the relevance of the recent Machine Learning (ML) literature for eco- nomics and econometrics. First we discuss the differences in ...
[PDF] Complexity and Satisficing: Theory with Evidence from Chess
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Our experimental design thus tests the idea that higher object complexity is associated with more classification errors, independent of whichever choice.
(1) corresponds to an error in perception. Moreover, the variance of the error term depends directly on the object's inherent complexity and the DM's skill ...
Mar 1, 2023 · Time series classification is a growing problem in different disciplines due to the progressive digitalization of the world.