An Improved P-SVM Model for Patent Application Filings Prediction

Sheng Xu; Tao Qi

doi:10.4028/www.scientific.net/AMR.255-260.2141

Paper Titles

Enhancing the Flexibility of Kademlia under Churn
p.2121

Turing Instability and Hopf Bifurcation for the General Brusselator System
p.2126

Ammonia Detection by Dye Immobilized Microstructured Optical Fiber
p.2131

Application Research of Fuzzy Control for AC Motor on the PLC Platform
p.2136

An Improved P-SVM Model for Patent Application Filings Prediction
p.2141

Super-Resolution Reconstruction of Video Sequences by GST-Driven Adaptive Filtering
p.2145

Research on Pyrolysis Analysis and Morphology Change of Waste Computer Mainboard
p.2150

Detection of Laser Direction and Wavelength Using the Unitized Wedge
p.2155

A Novel Variable Step-Size Algorithm for Implicit Integration
p.2159

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 255-260An Improved P-SVM Model for Patent Application...

An Improved P-SVM Model for Patent Application Filings Prediction

Abstract:

The patent applications filings (PAF) are complex to conduct due to its nonlinearity of influenced factors. A novel way about nonlinear regression modeling of PAF with the potential support vector machines (P-SVM) is presented in this study. Grey theory is a truly multidisciplinary and generic theory that deals with systems that are characterized by poor information. However, the application combining grey system theory and P-SVM for PAF prediction is rare. In this study, a grey potential support vector machines with genetic algorithms (GPSVMG) is proposed to forecast PAF. In addition, Grey system is used to add a grey layer before neural input layer and white layer after P-SVM layer. Genetic algorithms (GAs) are used to determine free parameters of P-SVM. The experiments show that the GPSVMG model is outperformed not only single grey model but also SVM with genetic algorithms (SVMG) model and potential support vector machines with genetic algorithms (PSVMG) model. So, PAF prediction based on GPSVMG is of validity and feasibility.