4 research outputs found
An End-to-End Approach for Training Neural Network Binary Classifiers on Metrics Based on the Confusion Matrix
While neural network binary classifiers are often evaluated on metrics such
as Accuracy and -Score, they are commonly trained with a cross-entropy
objective. How can this training-testing gap be addressed? While specific
techniques have been adopted to optimize certain confusion matrix based
metrics, it is challenging or impossible in some cases to generalize the
techniques to other metrics. Adversarial learning approaches have also been
proposed to optimize networks via confusion matrix based metrics, but they tend
to be much slower than common training methods. In this work, we propose to
approximate the Heaviside step function, typically used to compute confusion
matrix based metrics, to render these metrics amenable to gradient descent. Our
extensive experiments show the effectiveness of our end-to-end approach for
binary classification in several domains
Intelligent Sensors for Human Motion Analysis
The book, "Intelligent Sensors for Human Motion Analysis," contains 17 articles published in the Special Issue of the Sensors journal. These articles deal with many aspects related to the analysis of human movement. New techniques and methods for pose estimation, gait recognition, and fall detection have been proposed and verified. Some of them will trigger further research, and some may become the backbone of commercial systems