Recent advances in error rate estimation
Pattern Recognition Letters
Solving of optimization and identification problems by the committee methods
Pattern Recognition
Automatic Pattern Recognition: A Study of the Probability of Error
IEEE Transactions on Pattern Analysis and Machine Intelligence
Small Sample Size Effects in Statistical Pattern Recognition: Recommendations for Practitioners
IEEE Transactions on Pattern Analysis and Machine Intelligence
Neural networks and the bias/variance dilemma
Neural Computation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Meta Analysis of Classification Algorithms for Pattern Recognition
IEEE Transactions on Pattern Analysis and Machine Intelligence
Statistical Pattern Recognition: A Review
IEEE Transactions on Pattern Analysis and Machine Intelligence
Complexity of Classification Problems and Comparative Advantages of Combined Classifiers
MCS '00 Proceedings of the First International Workshop on Multiple Classifier Systems
On the Nonlinearity of Pattern Classifiers
ICPR '96 Proceedings of the International Conference on Pattern Recognition (ICPR '96) Volume IV-Volume 7472 - Volume 7472
Estimation of Dependences Based on Empirical Data: Springer Series in Statistics (Springer Series in Statistics)
Estimation of Classification Error
IEEE Transactions on Computers
Comparison of Two Classification Methodologies on a Real-World Biomedical Problem
Proceedings of the Joint IAPR International Workshop on Structural, Syntactic, and Statistical Pattern Recognition
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
Multiple Classification Systems in the Context of Feature Extraction and Selection
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Reduction of the Boasting Bias of Linear Experts
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Bias-Variance Analysis and Ensembles of SVM
MCS '02 Proceedings of the Third International Workshop on Multiple Classifier Systems
Classifier geometrical characteristic comparison and its application in classifier selection
Pattern Recognition Letters
In search of targeted-complexity problems
Proceedings of the 12th annual conference on Genetic and evolutionary computation
A novel training weighted ensemble (TWE) with application to face recognition
Applied Soft Computing
Distributed learning with data reduction
Transactions on computational collective intelligence IV
Artificial Intelligence in Medicine
Domains of competence of the semi-naive Bayesian network classifiers
Information Sciences: an International Journal
| Hi-index | 0.00 |
Multiple classifier methods are effective solutions to difficult pattern recognition problems. However, empirical successes and failures have not been completely explained. Amid the excitement and confusion, uncertainty persists in the optimality of method choices for specific problems due to strong data dependences of classifier performance. In response to this, I propose that further exploration of the methodology be guided by detailed descriptions of geometrical characteristics of data and classifier models.