This thesis presents two approaches to how automatic programming can be applied to the well-studied field of classification through the use of the automatic programming system, Automatic Design of Algorithms Through Evolution (ADATE)
Abstract—In this paper, we face the problem of constructing a robust feature space for automatic classification of signals from narrow-band in-air ultrasonic sensors. In consideration of the existing sensor bandwidth restrictions, the importance of
Multi-label problems arise in various domains such as multi-topic document categorization, pro- tein function prediction, and automatic image annotation. One natural way to deal with such problems is to construct a binary classifier for each label,
ECG (electrocardiogram) data classification has a great variety of applications in health monitoring and diagnosis facilitation. In this paper, previous work on automatic ECG data classification is overviewed, the idea of applying deep learning tool
This book includes the majority of the methods developed over the last two decades. The algorithms are systematically classified to five major categories: likelihood-based classifiers, distribution test-based classifiers, feature-based classifiers,
Model classification is essential to the management and reuse of 3D CAD models. Manual model classification is laborious and error prone. At the same time, the automatic classification methods are scarce due to the intrinsic complexity of 3D CAD mode
