Efficient adaptive learning for classification tasks with binary units
Neural Computation
Evaluating the Generalization Ability of Support Vector Machines through the Bootstrap
Neural Processing Letters
Input Feature Extraction for Multilayered Perceptrons Using Supervised Principal Component Analysis
Neural Processing Letters
Perceptron Learning Revisited: The Sonar Targets Problem
Neural Processing Letters
The Minimum Number of Errors in the N-Parity and its Solution with an Incremental Neural Network
Neural Processing Letters
Adaptive Model Selection for Digital Linear Classifiers
ICANN '02 Proceedings of the International Conference on Artificial Neural Networks
Digital Least Squares Support Vector Machines
Neural Processing Letters
Boosting interval based literals
Intelligent Data Analysis
Efficient Implementation of SVM Training on Embedded Electronic Systems
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
| Hi-index | 0.00 |
We study the classification of sonar targets first introduced by Gorman & Sejnowski (1988). We discovered thatnot only the training set and the test set of this benchmarkare both linearly separable, although by different hyperplanes, but that thecomplete set of patterns, training and test patterns together,is also linearly separable. The distances of the patterns tothe separating hyperplane determined by learning with the training set alone, and to the one determined by learning the complete data set, are presented.