Take complexity in visual inspection
Human Factors
Boolean functions with engineering applications and computer programs
Boolean functions with engineering applications and computer programs
A survey of automated visual inspection
Computer Vision and Image Understanding
The nature of statistical learning theory
The nature of statistical learning theory
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Machine Learning
Women go with the (optical) flow
Proceedings of the SIGCHI Conference on Human Factors in Computing Systems
The Journal of Machine Learning Research
Pattern Classification (2nd Edition)
Pattern Classification (2nd Edition)
Introduction to Human Factors and Ergonomics for Engineers (Human Factors and Ergonomics Series)
Introduction to Human Factors and Ergonomics for Engineers (Human Factors and Ergonomics Series)
Human-machine interaction issues in quality control based on online image classification
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
Mining problem-solving strategies from HCI data
ACM Transactions on Computer-Human Interaction (TOCHI)
Assessment of the influence of adaptive components in trainable surface inspection systems
Machine Vision and Applications - Integrated Imaging and Vision Techniques for Industrial Inspection
Automated Visual Inspection: A Survey
IEEE Transactions on Pattern Analysis and Machine Intelligence
| Hi-index | 0.07 |
While machine learning is most often concerned with learning from humans, the fact that human behavior systematically differs for (groups of) people with different gender, age, education or cultural background is widely ignored. Obviously, such differences are reflected in the training humans provide to machine learning algorithms that in turn affects the induced models. A coherent set of experiment design and analysis methods is presented which was applied for studying gender differences in visual inspection decision making. Detailed results from a study with 50 female and 50 male subjects are reported. Although immediate performance measures were almost equal, highly significant differences in the structure of induced decision trees have been found (p=0.00005). This demonstrates the value of our contribution for researchers intending to investigate the otherwise hidden structure of cognitive gender differences rather than their merits.