In this paper, we introduced an efficient method to determine the optical properties of the two-layer medium from the steady-state spatially resolved diffuse reflectance, which is based on the theory of support vector machine (SVM).

This paper presents support vector machines(SVM),a new neural network algorithm,to predict bus arrival time.

With structural risk minimization principles,support vector machine(SVM) has excellent generalization ability,good forecasting accuracy and robustness.

Based on the improved dynamic independent component analysis(ICA) and the support vector machine(SVM),a modeling method for the prediction of silicon content in blast furnace hot metal is proposed in this paper.

In this paper,an improved K-means clustering algorithm is applied in the training of Support Vector Machine(SVM).

For the vibration signals of aero—engine,a fault diagnosis method based on independent component analysis(ICA) and support vector machine(SVM) is proposed.

On the basis of the standard SVM for regression, the dynamic ε-SVM method was proposed to establish precise mathematical models to describe the behavior of biochemistry systems, namely each training sample used different error.

SVD and SLSI are used to reduce the dimension of feature space,SVM algorithm is employed to classify the feature vectors of testing collection,and the categorical accuracy and macro-average F1 ar.

SVM algorithm use only labeled data to build a model for the classifier,then the EMBAYES classifier trains the model by a smaller number of labeled data augment with a large number of unlabeled data and ensures the accuracy.

In this paper,as the theory directing at minor samples,the applied aspects of SVM algorithm in watermarking detection are concerned.