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
Classifier's Complexity Control while Training Multilayer Perceptrons
Proceedings of the Joint IAPR International Workshops on Advances in Pattern Recognition
An Experimental Comparison of Fixed and Trained Fusion Rules for Crisp Classifier Outputs
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
Experts' Boasting in Trainable Fusion Rules
IEEE Transactions on Pattern Analysis and Machine Intelligence
Training a reciprocal-sigmoid classifier by feature scaling-space
Machine Learning
Trainable fusion rules. II. Small sample-size effects
Neural Networks
IEEE/ACM Transactions on Computational Biology and Bioinformatics (TCBB)
Evolution of Multi-class Single Layer Perceptron
ICANNGA '07 Proceedings of the 8th international conference on Adaptive and Natural Computing Algorithms, Part II
ICIAR '08 Proceedings of the 5th international conference on Image Analysis and Recognition
Chromatographic Pattern Recognition Using Optimized One-Class Classifiers
IbPRIA '09 Proceedings of the 4th Iberian Conference on Pattern Recognition and Image Analysis
Pattern Recognition
Classifiers fusion in recognition of wheat varieties
MCS'07 Proceedings of the 7th international conference on Multiple classifier systems
MCS'03 Proceedings of the 4th international conference on Multiple classifier systems
Multicategory nets of single-layer perceptrons: complexity and sample-size issues
IEEE Transactions on Neural Networks
Multiclass mineral recognition using similarity features and ensembles of pair-wise classifiers
IEA/AIE'10 Proceedings of the 23rd international conference on Industrial engineering and other applications of applied intelligent systems - Volume Part II
MMES'10 Proceedings of the 2010 international conference on Mathematical models for engineering science
GAVTASC'11 Proceedings of the 11th WSEAS international conference on Signal processing, computational geometry and artificial vision, and Proceedings of the 11th WSEAS international conference on Systems theory and scientific computation
Prediction of commodity prices in rapidly changing environments
ICAPR'05 Proceedings of the Third international conference on Advances in Pattern Recognition - Volume Part I
An excellent feature selection model using gradient-based and point injection techniques
ICONIP'06 Proceedings of the 13th international conference on Neural Information Processing - Volume Part II
Generalization error of multinomial classifier
SSPR'06/SPR'06 Proceedings of the 2006 joint IAPR international conference on Structural, Syntactic, and Statistical Pattern Recognition
The class imbalance problem in TLC image classification
ICIAR'06 Proceedings of the Third international conference on Image Analysis and Recognition - Volume Part II
Functional model of criminality: simulation study
CONTEXT'05 Proceedings of the 5th international conference on Modeling and Using Context
Accuracy of MLP based data visualization used in oil prices forecasting task
ICIAP'05 Proceedings of the 13th international conference on Image Analysis and Processing
Synthetic pattern generation for imbalanced learning in image retrieval
Pattern Recognition Letters
Boosted Pre-loaded Mixture of Experts for low-resolution face recognition
International Journal of Hybrid Intelligent Systems
| Hi-index | 0.00 |
The relation between classifier complexity and learning set size is very important in discriminant analysis. One of the ways to overcome the complexity control problem is to add noise to the training objects, increasing in this way the size of the training set. Both the amount and the directions of noise injection are important factors which determine the effectiveness for classifier training. In this paper the effect is studied of the injection of Gaussian spherical noise and k-nearest neighbors directed noise on the performance of multilayer perceptrons. As it is impossible to provide an analytical investigation for multilayer perceptrons, a theoretical analysis is made for statistical classifiers. The goal is to get a better understanding of the effect of noise injection on the accuracy of sample-based classifiers. By both empirical as well as theoretical studies, it is shown that the k-nearest neighbors directed noise injection is preferable over the Gaussian spherical noise injection for data with low intrinsic dimensionality