scholarly journals Parameter Identification of Ship Maneuvering Models Using Recursive Least Square Method Based on Support Vector Machines

2017 ◽  
Vol 11 (1) ◽  
pp. 23-29 ◽  
Author(s):  
Man Zhu ◽  
Axel Hahn ◽  
Yuanqiao Wen ◽  
A. Bolles
Keyword(s):  
Support Vector Machines ◽  
Parameter Identification ◽  
Least Square Method ◽  
Least Square ◽  
Support Vector ◽  
Recursive Least Square ◽  
Ship Maneuvering ◽  
Vector Machines ◽  
Recursive Least Square Method

A Study of the Screening Efficiency of a Probability Sieve Based on Higher-Order Spectrum Analysis and Support Vector Machines

2011 ◽  
Vol 291-294 ◽  
pp. 2089-2093
Author(s):  
Zheng Zhong Shi ◽  
Yi Jian Huang
Keyword(s):  
Support Vector Machines ◽  
Least Square Method ◽  
Higher Order ◽  
Least Square ◽  
Support Vector ◽  
Vibration Signals ◽  
Order Spectrum ◽  
Vector Machines ◽  
Screening Efficiency ◽  
Relative Errors

Aiming at drawbacks of current methods for predicting the screening efficiency of probability sieve, this paper proposed a method of predict and study the screening efficiency of probability sieve based on higher-order spectrum(HOS) analysis and support vector machines(SVMs). First setting up trispectrum model with the vibration signals, then fitting out polynomial with least square method using the data which get out by the reconstruct power spectrum. Finaly, using support vector machines to predicting the screening efficiency with the coefficient of the polynomial as the sample input. The results show that the relative errors are all less than 2.4% and the absolute errors are all less than 0.021, which is ideal for efficiency forecast.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Sign in / Sign up

Export Citation Format

Share Document