Theory and application of cellular automata for pattern classification
Fundamenta Informaticae - Special issue on cellular automata
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata
Fundamenta Informaticae - Special issue on DLT'04
On Characterization of Attractor Basins of Fuzzy Multiple Attractor Cellular Automata
Fundamenta Informaticae
New Fast Decision Tree Classifier for Identifying Protein Coding Regions
ISICA '08 Proceedings of the 3rd International Symposium on Advances in Computation and Intelligence
International Journal of Bioinformatics Research and Applications
Design of SMACA: synthesis and its analysis through rule vector graph for web based application
International Journal of Intelligent Information and Database Systems
On Characterization of Attractor Basins of Fuzzy Multiple Attractor Cellular Automata
Fundamenta Informaticae
RBFFCA: A Hybrid Pattern Classifier Using Radial Basis Function and Fuzzy Cellular Automata
Fundamenta Informaticae - Special issue on DLT'04
Theory and Application of Cellular Automata For Pattern Classification
Fundamenta Informaticae - Cellular Automata
An improved multiple-attractor cellular automata classifier with a tree frame based on CART
Computers & Mathematics with Applications
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This paper enumerates a new approach to the solution of classification problems based on the properties of Additive Cellular Automata. Classification problem plays a major role in various fields of computer science, such as grouping of the records in database systems, detection of faults in VLSI circuits, image processing, and so on. The state-transition graph of Non-group Cellular Automata (CA) consists of a set of disjoint trees rooted at some cyclic states of unit cycle length - thus forming a natural classifier. First a scheme of classifying the patterns distributed into only two classes has been dealt with. This has been further extended for solution of the multiclass classification problem. The Multiclass Classifier saves on an average 34% of memory as compared to the straight-forward approach storing directly the class of each pattern. A regular, modular, and cascadable hardware implementation of the classifier has been presented which is highly suitable for VLSI realization. The design has been specified in Verilog and verified for functional correctness.