The nature of statistical learning theory
The nature of statistical learning theory
An application of one-class support vector machines in content-based image retrieval
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A Forecasting Model Based Support Vector Machine and Particle Swarm Optimization
PEITS '08 Proceedings of the 2008 Workshop on Power Electronics and Intelligent Transportation System
PSO-based single multiplicative neuron model for time series prediction
Expert Systems with Applications: An International Journal
Moderating the outputs of support vector machine classifiers
IEEE Transactions on Neural Networks
Power load forecasts based on hybrid PSO with Gaussian and adaptive mutation and Wv-SVM
Expert Systems with Applications: An International Journal
The hybrid forecasting model based on chaotic mapping, genetic algorithm and support vector machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Regression application based on fuzzy ν-support vector machine in symmetric triangular fuzzy space
Expert Systems with Applications: An International Journal
Journal of Computational and Applied Mathematics
Car assembly line fault diagnosis based on robust wavelet SVC and PSO
Expert Systems with Applications: An International Journal
Hybrid model based on SVM with Gaussian loss function and adaptive Gaussian PSO
Engineering Applications of Artificial Intelligence
Fault diagnosis model based on Gaussian support vector classifier machine
Expert Systems with Applications: An International Journal
Car assembly line fault diagnosis based on modified support vector classifier machine
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
The forecasting model based on modified SVRM and PSO penalizing Gaussian noise
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
A two-stage dynamic sales forecasting model for the fashion retail
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
An intelligent fast sales forecasting model for fashion products
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
The forecasting model based on fuzzy novel ν-support vector machine
Expert Systems with Applications: An International Journal
A sparse Gaussian process regression model for tourism demand forecasting in Hong Kong
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Information Sciences: an International Journal
Fast fashion sales forecasting with limited data and time
Decision Support Systems
Hi-index | 12.08 |
Aiming at the series with small samples, seasonal character, nonlinearity, randomicity and fuzziness, the existing support vector kernel does not approach the random curve of the sales time series in the L^2(R^n) space (quadratic continuous integral space). A new wavelet support vector machine (WN @n-SVM) is proposed based on wavelet theory and modified support vector machine. A particle swarm optimization (PSO) algorithm is designed to select the best parameters of WN @n-SVM model in the scope of constraint permission. The results of application in car sale series forecasting show that the forecasting approach based on the PSOWN @n-SVM model is effective and feasible, the comparison between the method proposed in this paper and other ones is also given which proves this method is better than PSOW @n-SVM and other traditional methods.