Prediction of Properties of Cu-15Ni-8Sn Alloys Based on Least Square Support Vector Machines

2013 ◽  
Vol 813 ◽  
pp. 479-483
Author(s):  
Shan Feng Fang ◽  
Ming Pu Wang
Keyword(s):  
Support Vector Machines ◽  
Predictive Accuracy ◽  
Copper Alloys ◽  
Least Square ◽  
Support Vector ◽  
Promising Alternative ◽  
Vector Machines ◽  
Artificial Neural Network Ann ◽  
Experimental Values ◽  
Leave One Out

A new model based on least square support vector machines (LSSVM) and capable of forecasting mechanical and electrical properties of Cu-15Ni-8Sn alloys has been proposed. Data mining and artificial intelligence techniques of copper alloys are used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, leave-one-out-cross-validation (LOOCV) technique is adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the artificial neural network (ANN) has been compared with the experimental values. The result shows that the LSSVM model provides slightly better capability of generalized prediction compared to ANN. The present calculated results are consistent with the experimental values, which suggest that the proposed LSSVM model is feasible and efficient and is therefore considerd to be a promising alternative method to forecast the variation of the hardness and electrical conductivity with aging temperature and aging time.

Prediction of the Hardness of Cu-3Ti-1Cr Alloy Using Least Square Support Vector Machines

2014 ◽  
Vol 789 ◽  
pp. 574-579
Author(s):  
San Feng Fang ◽  
Ming Pu Wang
Keyword(s):  
Support Vector Machines ◽  
Predictive Accuracy ◽  
Copper Alloys ◽  
Cold Work ◽  
Least Square ◽  
Support Vector ◽  
Vector Machines ◽  
Forecasting Performance ◽  
Experimental Values ◽  
Leave One Out

A new model based on least square support vector machines (LSSVM) and capable of forecasting the hardness of Cu-3Ti-1Cr alloy has been proposed. Data mining and artificial intelligence techniques were used to examine the forecasting capability of the model. In order to improve predictive accuracy and generalization ability of LSSVM model, leave-one-out-cross-validation (LOOCV) technique was adopted to determine the optimal hyper-parameters of LSSVM automatically. The forecasting performance of the LSSVM model and the partial least squares (PLS) regression integrated with radial basis function (RBF-PLS) has been compared with the experimental values. The result demonstrates that the LSSVM model is superior to the conventional RBF-PLS model in predicting the hardness of Cu-3Ti-1Cr alloy and of better generalization performance than the RBF-PLS model. The present calculated results are consistent with the experimental values. We would expect the proposed LSSVM model as a powerful tool to forecast the variation of the hardness of copper alloys with prior cold work, aging temperature and aging time.

Gaussian Processes for Classification: Mean-Field Algorithms

2000 ◽  
Vol 12 (11) ◽  
pp. 2655-2684 ◽  
Author(s):  
Manfred Opper ◽  
Ole Winther
Keyword(s):  
Support Vector Machines ◽  
Gaussian Processes ◽  
Disordered Systems ◽  
Binary Classification ◽  
Computational Cost ◽  
Mean Field ◽  
Strong Support ◽  
Support Vector ◽  
Vector Machines ◽  
Leave One Out

We derive a mean-field algorithm for binary classification with gaussian processes that is based on the TAP approach originally proposed in statistical physics of disordered systems. The theory also yields an approximate leave-one-out estimator for the generalization error, which is computed with no extra computational cost. We show that from the TAP approach, it is possible to derive both a simpler “naive” mean-field theory and support vector machines (SVMs) as limiting cases. For both mean-field algorithms and support vector machines, simulation results for three small benchmark data sets are presented. They show that one may get state-of-the-art performance by using the leave-one-out estimator for model selection and the built-in leave-one-out estimators are extremely precise when compared to the exact leave-one-out estimate. The second result is taken as strong support for the internal consistency of the mean-field approach.

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.

Support Vector Machine Classification applied on Weaning Trials Patients

2008 ◽  
pp. 1277-1282
Author(s):  
B.F. Giraldo ◽  
A. Garde ◽  
C. Arizmendi ◽  
R. Jané ◽  
I. Diaz ◽  
...  
Keyword(s):  
Mechanical Ventilation ◽  
Support Vector Machine ◽  
Support Vector Machines ◽  
Work Of Breathing ◽  
Selection Procedure ◽  
Respiratory Pattern ◽  
Support Vector ◽  
Vector Machines ◽  
Leave One Out ◽  
Pattern Variability

The most common reason for instituting mechanical ventilation is to decrease a patient’s work of breathing. Many attempts have been made to increase the effectiveness on the evaluation of the respiratory pattern by means of respiratory signal analysis. This work suggests a method of studying the lying differences in respiratory pattern variability between patients on weaning trials. The core of the proposed method is the use of support vector machines to classify patients into two groups, taking into account 35 features of each one, previously extracted from the respiratory flow. 146 patients from mechanical ventilation were studied: Group S of 79 patients with Successful trials, and Group F of 67 patients that Failed on the attempt to maintain spontaneous breathing and had to be reconnected. Applying a feature selection procedure based on the use of the support vector machine with leave-one-out cross-validation, it was obtained 86.67% of well classified patients into the Group S and 73.34% into Group F, using only eight of the 35 features. Therefore, support vector machines can be an interesting classification method in the study of the respiratory pattern variability.

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