Algorithms for clustering data
Algorithms for clustering data
Stable adaptive systems
Trace Inference, Curvature Consistency, and Curve Detection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Optimal Fuzzy Clustering
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computer Vision, Graphics, and Image Processing
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Adaptation and Learning in Automatic Systems
Adaptation and Learning in Automatic Systems
Evolutionary Approaches to Figure-Ground Separation
Applied Intelligence
Metric-Based Methods for Adaptive Model Selection and Regularization
Machine Learning
Figure-Ground Discrimination: A Combinatorial Optimization Approach
IEEE Transactions on Pattern Analysis and Machine Intelligence
Ground from Figure Discrimination
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
| Hi-index | 0.14 |
The production of robust classifiers by combining supervised training with unsupervised training is discussed. A supervised training phase exploits statistically scene invariant labeled data to produce an initial classifier. This is followed by an unsupervised training phase that exploits clustering properties of unlabeled data. This two-phase process is termed mixed adaptation. A probabilistic model supporting this technique is presented along with examples illustrating mixed adaptation. These examples include the detection of unspecified dotted curves in dotted noise and the detection and classification of vehicles in cinematic sequences of infrared imagery.