A Method of Combining Multiple Experts for the Recognition of Unconstrained Handwritten Numerals
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Lexicon Driven Approach to Handwritten Word Recognition for Real-Time Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal Linear Combination of Neural Networks for Improving Classification Performance
IEEE Transactions on Pattern Analysis and Machine Intelligence
Large-Scale Evaluation of Multimodal Biometric Authentication Using State-of-the-Art Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Optimal classifier combination rules for verification and identification systems
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
Fusion for multimodal biometric identification
AVBPA'05 Proceedings of the 5th international conference on Audio- and Video-Based Biometric Person Authentication
Utilizing independence of multimodal biometric matchers
MRCS'06 Proceedings of the 2006 international conference on Multimedia Content Representation, Classification and Security
Use of Identification Trial Statistics for the Combination of Biometric Matchers
IEEE Transactions on Information Forensics and Security
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management
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Cluster ensembles provide us with a versatile alternative to individual clustering algorithms. In structural pattern recognition, however, cluster ensembles have been rarely studied. In the present paper a general methodology for creating structural cluster ensembles is proposed. Our representation formalism is based on graphs and includes strings and trees as special cases. The basic idea of our approach is to view the dissimilarities of an input graph g to a number of prototype graphs as a vectorial description of g . Randomized prototype selection offers a convenient possibility to generate m different vector sets out of the same graph set. Applying any available clustering algorithm to these vector sets results in a cluster ensemble with m clusterings which can then be combined with an appropriate consensus function. In several experiments conducted on different graph sets, the cluster ensemble shows superior performance over two single clustering procedures.