C4.5: programs for machine learning
C4.5: programs for machine learning
Machine learning, neural and statistical classification
Machine learning, neural and statistical classification
Feature Selection via Discretization
IEEE Transactions on Knowledge and Data Engineering
R-MINI: An Iterative Approach for Generating Minimal Rules from Examples
IEEE Transactions on Knowledge and Data Engineering
An Implementation of Logical Analysis of Data
IEEE Transactions on Knowledge and Data Engineering
Binary Rule Generation via Hamming Clustering
IEEE Transactions on Knowledge and Data Engineering
Approximation properties of positive boolean functions
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Evaluating Switching Neural Networks for Gene Selection
WILF '07 Proceedings of the 7th international workshop on Fuzzy Logic and Applications: Applications of Fuzzy Sets Theory
A Constructive Technique Based on Linear Programming for Training Switching Neural Networks
ICANN '08 Proceedings of the 18th international conference on Artificial Neural Networks, Part II
Evaluating switching neural networks through artificial and real gene expression data
Artificial Intelligence in Medicine
ISMM '09 Proceedings of the 9th International Symposium on Mathematical Morphology and Its Application to Signal and Image Processing
Switching Neural Network: An application to Regression Problems
Proceedings of the 2011 conference on Neural Nets WIRN10: Proceedings of the 20th Italian Workshop on Neural Nets
Approximation properties of positive boolean functions
WIRN'05 Proceedings of the 16th Italian conference on Neural Nets
Journal of Mathematical Imaging and Vision
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A new connectionist model, called Switching Neural Network (SNN), for the solution of classification problems is presented. SNN includes a first layer containing a particular kind of A/D converters, called latticizers, that suitably transform input vectors into binary strings. Then, the subsequent two layers of an SNN realize a positive Boolean function that solves in a lattice domain the original classification problem. Every function realized by an SNN can be written in terms of intelligible rules. Training can be performed by adopting a proper method for positive Boolean function reconstruction, called Shadow Clustering (SC). Simulation results obtained on the StatLog benchmark show the good quality of the SNNs trained with SC.