When is it safe to use synthetic data in supervised classification? Trainable classifier technologies require large representative training sets consisting of samples labeled with...
Both example-based and model-based approaches for classifying contour shapes can encounter difficulties when dealing with classes that have large nonlinear variability, especially...
Many previous studies have investigated gender classification in well-lit frontal images. In this paper we consider images where the pose, expression and lighting are relatively u...
The meta-learner MLR (Multi-response Linear Regression) has been proposed as a trainable combiner for fusing heterogeneous baselevel classifiers. Although it has interesting prope...
Abstract. Clinical requirements for quick accessibility to reputable, up-todate information have increased the importance of web accessible digital libraries for this user communit...