Machine Learning
Image-based multimodal face authentication
Signal Processing
Integrating Faces and Fingerprints for Personal Identification
IEEE Transactions on Pattern Analysis and Machine Intelligence
Person Identification Using Multiple Cues
IEEE Transactions on Pattern Analysis and Machine Intelligence
Transductive Inference for Text Classification using Support Vector Machines
ICML '99 Proceedings of the Sixteenth International Conference on Machine Learning
Information fusion in biometrics
Pattern Recognition Letters - Special issue: Audio- and video-based biometric person authentication (AVBPA 2001)
A classification paradigm for distributed vertically partitioned data
Neural Computation
Transductive Methods for the Distributed Ensemble Classification Problem
Neural Computation
An Extension of Iterative Scaling for Decision and Data Aggregation in Ensemble Classification
Journal of VLSI Signal Processing Systems
Adaptive mixtures of local experts
Neural Computation
Fusion of face and speech data for person identity verification
IEEE Transactions on Neural Networks
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We consider ensemble classification when there is no common labeled data for designing the function which aggregates classifier decisions. In recent work, we dubbed this problem distributed ensemble classification, addressing when local classifiers are trained on different (e.g., proprietary, legacy) databases or operate on different sensing modalities. Typically, fixed (untrained) rules of classifier combination such as voting methods are used in this case. However, these may perform poorly, especially when (i) the local class priors, used in training, differ from the true (test batch) priors and (ii) classifier decisions are statistically dependent. Alternatively, we proposed several transductive methods, optimizing the combining rule for objective functions measured on the test batch. We proposed both maximum likelihood (ML) and constraint-based (CB) objectives and found that CB achieved superior performance. Here, we develop CB extensions (i) for sequential decisionmaking and (ii) exploiting additional class information contained in the local classifier feature vectors. The new sequential method is applied to biometric authentication. We demonstrate these new CB methods achieve better ensemble decision accuracy than methods which apply fixed rules in combining classifier decisions.