High Speed Unknown Word Prediction Using Support Vector Machine for Chinese Text-to-Speech Systems

2005 ◽  
pp. 509-517 ◽  
Author(s):  
Juhong Ha ◽  
Yu Zheng ◽  
Byeongchang Kim ◽  
Gary Geunbae Lee ◽  
Yoon-Suk Seong
Keyword(s):  
Support Vector Machine ◽  
Chinese Text ◽  
High Speed ◽  
Support Vector ◽  
Text To Speech ◽  
Word Prediction ◽  
Unknown Word

Short-Time Fluctuation Characteristic and Combined Forecasting of High-Speed Railway Passenger Flow Based on EEMD

2013 ◽  
Vol 409-410 ◽  
pp. 1071-1074
Author(s):  
Xiu Shan Jiang ◽  
Rui Feng Zhang ◽  
Liang Pan
Keyword(s):  
Support Vector Machine ◽  
Empirical Mode Decomposition ◽  
High Speed ◽  
Ensemble Empirical Mode Decomposition ◽  
Support Vector ◽  
High Speed Railway ◽  
Passenger Flow ◽  
Mode Decomposition ◽  
Railway Passenger ◽  
Short Term Forecasting

Take Wuhan-Guangzhou high-speed railway for example. By adopting the empirical mode decomposition (EMD) attempt to analyze mode from the perspective of volatility of high speed railway passenger flow fluctuation signal. Constructed the ensemble empirical mode decomposition-gray support vector machine (EEMD-GSVM) short-term forecasting model which fuse the gray generation and support vector machine with the ensemble empirical mode decomposition (EEMD). Finally, by the accuracy of predicted results, explains the EEMD-GSVM model has the better adaptability.

Application of TSVM Incremental Learning in Web Text Categorization

2010 ◽  
Vol 159 ◽  
pp. 556-561 ◽  
Author(s):  
Zhi Juan Jia ◽  
Wei Xu Hao ◽  
Xiang Yu Zhang
Keyword(s):  
Support Vector Machine ◽  
Chinese Text ◽  
Learning Process ◽  
Incremental Learning ◽  
Text Categorization ◽  
Experimental Studies ◽  
Current Situation ◽  
Support Vector ◽  
Key Technology ◽  
Categorization Model

Based on the research of the current situation in text categorization, this paper has drawn an inductive conclusion on the key technology of text category, carried out an exploration on the theory of transductive support vector machine (TSVM) as well as the categorizing process of incremental learning, established web Chinese text categorization model on the basis of TSVM incremental learning, and explained the learning process of incremental learning in Chinese text categorization. Experimental studies show that incremental learning has significant effect on improving the categorizing performance.

Application of ACO-SVM in Chinese Text Feature Selection

2012 ◽  
Vol 155-156 ◽  
pp. 770-775
Author(s):  
Jin Xiu Cui ◽  
Xiao Xia Huang
Keyword(s):  
Support Vector Machine ◽  
Feature Selection ◽  
Chinese Text ◽  
Classification Accuracy ◽  
Considerable Increase ◽  
Experimental Results ◽  
Support Vector ◽  
Text Feature

The algorithm proposed in this paper applies ACO in combination with support vector machine (SVM) in Chinese text feature selection. It obtains classifier models for each category at last. The experimental results show that the proposed method is feasible and lead to a considerable increase of classification accuracy.

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