Accuracy vs. Simplicity: A Complex Trade-Off

Accuracy vs. Simplicity: A Complex Trade-Off∗

Inductive learning aims at finding general rules that hold true in a database. Targeted learning seeks rules for the prediction of the value of a variable based on the values of others, as in the case of linear or non-parametric regression analysis. Non-targeted learning finds regularities without a specific prediction goal. We model the product of non-targeted learning as rules that state that...

Accuracy - precision trade - off in visual

Speed accuracy trade-off under response deadlines

Perceptual decision making has been successfully modeled as a process of evidence accumulation up to a threshold. In order to maximize the rewards earned for correct responses in tasks with response deadlines, participants should collapse decision thresholds dynamically during each trial so that a decision is reached before the deadline. This strategy ensures on-time responding, though at the c...

Model Complexity-Accuracy Trade-off for a Convolutional Neural Network

Convolutional Neural Networks(CNN) has had a great success in the recent past, because of the advent of faster GPUs and memory access. CNNs are really powerful as they learn the features from data in layers such that they exhibit the structure of the V-1 features of the human brain. A huge bottleneck in this case is that CNNs are very large and have a very high memory footprint, and hence they ...

Toward a unified view of the speed-accuracy trade-off

(2015) Toward a unified view of the speed-accuracy trade-off. Hasty decisions often lead to poor choices, whereas accurate decisions are ineffective if they take too long. Thus, good choices require cognitive mechanisms to determine the appropriate balance between speed and accuracy, and to control decision processing accordingly. This balance is referred to as the speed-accuracy trade-off (SAT...