Unsupervised Clustering Based Reduced Support Vector Machines;

Hyper-sphere support vector machine is an important method for unbalanced classification which is an important issue in biomedical engineering such as tongue image classification in traditional chinese medicine.

Support Vector Machine (SVM) is a pattern classification technique.

Novel wellbore trajectory prediction method based on support vector machine;

A novel method for predicting wellbore trajectory based on support vector machine;

Application of support vector machine in evaluation of Cangzhou sustainable development of water resource;

Kernel-based Support Vector Machine (Kernel-SVM) was chosen as classifier.

Then, a content-based video classifier is designed by introducing the active relevance feedback technique into Support Vector Machine (SVM) for the cartoon video detection.

An uplink Multi-Carrier CDMA(MC-CDMA) multi-user detector based on Incremental Support Vector Machine(ISVM) is proposed in this paper.

Boiler combustion optimization based upon support vector machines(SVM)modeling and nonlinear model predictive control(MPC)technology was applied to control the process of boiler combustion in close-loop.

By analyzing the generalities and specialities of rough sets (RS) and support vector machines (SVM) in knowledge representation and process of classification, a minimum decision network combining RS with SVM in intelligence processing is investigated, and a kind of SVM system on RS is proposed for forceasting.

A speech music discrimination system based on support vector machines has been built by the specific features.

For improving adaptability and accuracy of validating text,the application of two-dimension principal component analysis(2DPCA) for video frame processing is investigated and a novel 2DPCA and support vector machine(SVM) based approach for validating t.

And by setting the value of each attribute the literal sequences are transformed into numeric vectors,based on which a Support Vector Machine(SVM) model is constructed to predict splice sites.

Then,support vector regression is used firstly to investigate the quantit.

In this paper,we propose to estimate abundances from hyperspectral image using support vector regression(SVR) method.

Applying the geometric approach to support vector regression(SVR) has constructed a model of fuzzy support vector regression(FSVR) based on the data with confidence.