Constructive Methods for a New Classifier Based on a Radial-Basis-Function Neural Network Accelerated by a Tree
نویسندگان
چکیده
We present a new constructive algorithm for building Radial-Basis-Function (RBF) network classiiers and a tree based associated algorithm for fast processing of the network. This method, named Constructive Tree Radial-Basis-Function (CTRBF), allows to build and train a RBF network in one pass over the training data set. The training can be in supervised or unsupervised mode. Furthermore, the algorithm is not restricted to xed input size problems. Several construction and pruning strategies are discussed. We tested and compared this algorithm with classical RBF and multilayer perceptrons on a real world problem: on-line handwritten character recognition. While instantaneous incremental learning is the major property of the architecture, the tree associated to the RBF network gives impressive speed improvement with minimal performance losses. Speed-up factors of 20 over classical RBF have been obtained.
منابع مشابه
Training Radial Basis Function Neural Network using Stochastic Fractal Search Algorithm to Classify Sonar Dataset
Radial Basis Function Neural Networks (RBF NNs) are one of the most applicable NNs in the classification of real targets. Despite the use of recursive methods and gradient descent for training RBF NNs, classification improper accuracy, failing to local minimum and low-convergence speed are defects of this type of network. In order to overcome these defects, heuristic and meta-heuristic algorith...
متن کاملImpact of Structural Components of Market on the Markup Level Based on Radial Basis Neural Network and Fuzzy Logic
This paper aims to evaluate the impact of several indices of market structure including entry to barrier, economies of scale and concentration degree on 140 active industries using the digit. Accordingly, we apply three methods including cost disadvantages ratio ( ), Herfindahl–Hirschman concentration index ( ) and Comanor and Willson criterion in order to assess the economies of scale and usin...
متن کاملImproving Accuracy of DGPS Correction Prediction in Position Domain using Radial Basis Function Neural Network Trained by PSO Algorithm
Differential Global Positioning System (DGPS) provides differential corrections for a GPS receiver in order to improve the navigation solution accuracy. DGPS position signals are accurate, but very slow updates. Improving DGPS corrections prediction accuracy has received considerable attention in past decades. In this research work, the Neural Network (NN) based on the Gaussian Radial Basis Fun...
متن کاملDetection and Classification of Breast Cancer in Mammography Images Using Pattern Recognition Methods
Introduction: In this paper, a method is presented to classify the breast cancer masses according to new geometric features. Methods: After obtaining digital breast mammogram images from the digital database for screening mammography (DDSM), image preprocessing was performed. Then, by using image processing methods, an algorithm was developed for automatic extracting of masses from other norma...
متن کاملInvestigating the performance of machine learning-based methods in classroom reverberation time estimation using neural networks (Research Article)
Classrooms, as one of the most important educational environments, play a major role in the learning and academic progress of students. reverberation time, as one of the most important acoustic parameters inside rooms, has a significant effect on sound quality. The inefficiency of classical formulas such as Sabin, caused this article to examine the use of machine learning methods as an alternat...
متن کامل