Multilayer feedforward networks are universal approximators
Neural Networks
The nature of statistical learning theory
The nature of statistical learning theory
Fast training of support vector machines using sequential minimal optimization
Advances in kernel methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Machine Learning
Simulation of Vegetable Populations Dynamics Based on Cellular Automata
ACRI '01 Proceedings of the 5th International Conference on Cellular Automata for Research and Industry
Improvements to Platt's SMO Algorithm for SVM Classifier Design
Neural Computation
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
A Neuro-Genetic Framework for Pattern Recognition in Complex Systems
Fundamenta Informaticae - Membrane Computing
Hi-index | 0.00 |
The paper presents an empirical study aiming at evaluating and comparing several Machine Learning (ML) classification techniques in the automatic recognition of known patterns The main motivations of this work is to select best performing classification techniques where target classes are based on the occurrence of known patterns in configurations of a forest system modeled according to Cellular Automata Best performing ML classifiers will be adopted for the study of ecosystem dynamics within an interdisciplinary research collaboration between computer scientists, biologists and ecosystem managers (Cellular Automata For Forest Ecosystems – CAFFE project) One of the main aims of the CAFFE project is the development of an analysis method based on recognition in CA state configurations of spatial patterns whose interpretations are inspired by the Chinese Go game.