support vector

The results show that this method is feasible and the classification accuracy and speed is better than traditional support vector machine.

Pointed out that the concept of knowledge, general classification algorithms and support vector machine classifier.

However, as a newer theory of machine learning field, support Vector Machine is still being developed and constant improved.

In the classification experiment, we find that the number of the support vector is far less than the number of the training sample number.

This method took some of the non-support-vector samples out of the training set; therefore efficiently reduced the set size.

Simulation results show that this method has a better robustness, and improve the generalization ability of fuzzy support vector.

The selection method of kernel in the information entropy support vector machine model is one of our in-depth researches.

Initial goal concept learned by standard support vector machine algorithm was updated by an updating model.

RVM to support vector machines with the same function form of sparse probabilistic model to predict the unknown function, or classification.

The theories show that linear support vector classification satisfies second order sufficient condition property to a great degree.

and a support vector machine regression (SVR) technique is introduced to establish the model of power equipment insulation status change.

Based on information extraction, support vector machines and K-NN were used for protein name extraction from biomedical literatures.

This paper studies the human face detection by support vector classifier using histograms of color, color edge and its orientation.

Support Vector Machine is proven to be a novel approach to model the cutting depth of abrasive water jet.

With the combination with a Support Vector Machine, we design and implement the improved text classifier.

The last topic is about a trainable object detection system using at its core a Support Vector Machine classifier.

this class , taught by the guest speaker , is devoted to the optimization problems underlying the implementation of support vector machines.

Information classification model established a face to face simple step-form of support vector machine web text classification.

Support Vector Machines via a decision boundary optimization, has proved is an excellent classifier in the model recognition.

Experimental result indicated that, support vector machine used in flight segment volume is effective.

We first give an introduction of Statistical Learning Theory and Support Vector Machines and its application to modeling.

The support vector machine(SVM)combined with the k-nearest neighbor(KNN)algorithm is applied to solar proton event prediction.

Then, the square support vector machines were used to recognize combustion states.

When traditional support vector machines separate data containing noises, the obtained hyperplane is not an optimal one.

A sort of incremental learning algorithm for support vector machine based on clustering was proposed.

Secondly, analyzed the characteristics of term load forecasting, using support vector regression algorithm for long-term load forecasting.

As the newest branch of statistic learning theory, support vector machine(SVM) have become increasingly popular tools.

This paper introduces the theory of support vector machine (SVM) arid applies it in image classification.

On this base, a novel segmentation algorithm using support vector machine is presented for segmentation.

Then, support vector machine is selected to classify aiming at clinical ECG data. Finally, classifications combination approach is analyzed.

The dissertation mainly aims at applying support vector machine (SVM) and kernel principal component analysis (KPCA) to intrusion detection.

Directed acyclic graph support vector (DAG-SVMS) multi-category classification methods, is a new multi-category classification methods.

For improving the generalization performance of support vector machine(SVM) effectively, two ensemble algorithms are proposed to train SVM.

Support vector machines (SVM), a power machine learning algorithm, were applied to the self-learning of FLC.

Compared with the ARIMA, regression analysis, gray model, support vector machines and neural network prediction methods.

The paper presents a strategy of reducing the size of the training sample set for support vector machines (SVM).

Thus, we provide a theoretical foundation for establishing complete linear support vector ordinal regression machines.

The support vector clustering(SVC)algorithm was introduced to detect linear modulation frequency(LFM) signal and estimate its parameter.

Extensive comparisons have shown that support vector machines obtain good generalization performance on various data sets.

In this thesis, segmentation technique of support vector machine is analyzed and researched basic on partly main segmentation technique.

Support vector machine(SVM) is a study machine developing from statical learning theory, which has splendid regression feature.

The Support Vector Machine (SVM) method and its application to short-term load forecast are presented.

This paper majorly discussed the application of the support vector machine SVM in rice water requirement forecast.

Thus an approach that applied immune algorithm to optimize parameters of SVM (Support Vector Machine) and feature selection was proposed.

In this article, we have studied application of the fuzzy support vector machines in the beforehand alarm of grain safe keeping.

Support vector machine classification is realized by least square algorithm and numerical simulation is performed.

However, the training procedure of support vector machines amounts to solving a constrained quadratic programming.

The high computational cost has become a bottleneck of support vector machines applied to large scale problems.

For this reason, several important properties of kernel function are discussed on the basis of support vector machine.

Experimental results show that support vector machine approach is a promising technique for image segmentation.

The combination of wavelet analysis and support vector machine is introduced to automatically diagnose power supply line fault.

The experiments show using fuzzy support vector machine significantly improves the overall recognition rate.

Min-Max Modular Support Vector Machine(M3-SVM) is an "divide and conquer" based algorithm which can effectively solve large-scale problems.

Support vector machines (SVM) are used as the verification algorithm for its excellent performance of classification.

Compared to the classical support vector machines based method, the proposed method performs better in text detection.

Artificial Neural Networks and Support Vector Machine were selected to test classification accuracy and the test results were analyzed.

The classification ability was verified by support vector machines algorithm with various kernel functions.

Support vector machine, which has high precision of categorization and excellent generalization ability, is used to categorize text.

In response to above problems, this paper proposes a fracture-cave reservoir prediction method relying on support vector machine.

Then the characteristic was classified and predicted to generate semantic vector using support vector machine.

SVM(Support Vector Machines) is a new science subject of maths field in recent years which has extensive and promising applied foreground.

Finally, forecasting method based on support vector machine applies in the system of "Flight Optimization & Revenue Maximization" .

This paper introduces the machine learning procedure of training support vector machine by using linear particle swarm optimization.

Support Vector Machine(SVM) method is a surface fitting algorithm in a way.

After the feature vector is available, support vector machine(SVM) is used for classification.

The principle and algorithm of K- nearest neighbor, Naive Bayes, Support Vector Machine Nerve Network etc. are elaborated.

Support Vector Machine (SVM) is a new technique in data mining, but Support Vector Regression (SVR) is the applying of SM in regression.

Using a bootstrap procedure, we design a mechanism to extract significant factors from the support vector approach.

By using support vector value contourlet transform, cloud information was filtered to obtain the ground information.

Support vector AAM based iterative learning algorithm for gender classification .

In this study, Total-margin Adaptive Fuzzy Support Vector Machine (TAFSVM) of good quality was proposed.

Support Vector Machine is a new machine learning algorithm for classification and regression question.

The results shows that the Bayesian Belief Network has better performance than the Decision Tree and Support Vector Machine.

In this paper, the support vector machine method is used to forecast ice jam stage, and the results are accordant with the fact.

Support vector machine can reflect the complex mapping relation between input factors and the output results perfectly.

The model solved a convex quadratic programming similar to standard support vector machine algorithm.

To find out their relationship, this paper, support vector machine method, the data collected in different analysis of nuclear function.

The support vector machine based speaker verification models are trained on the enrolled speaker and the background model.

This paper mainly studied the application of Support Vector Machines(SVM) method on reservoir prediction.

MissSVM package solution is to use multi-instance semi-supervised support vector machine learning problems.

With the aspect of prediction of highs and lows, both smooth support vector regression and back propagation network are above 75% accuracy.

And support vector machine model is used to estimate the unmeasured process parameters that are difficult to model with first principle.

By applying smooth support vector regression and back propagation network in the prediction model can beat the market.

Researches a novel support vector fuzzy regression machine that combines the merits of both models.

Finally, support vector machine (SVM) was adopted for automatic determination for short-time sleep data.

We also compare kernel function's performance, and adjust optimal parameters to Support Vector Machines.

A CEDT opportunities and support vector machine classification and face recognition.

In this paper, the authors present a method combined rough set ( RS ) with least square support vector regression (LS-SVR).

It is studied the application of independent component analysis and support vector machine to fault diagnosis of induction motor.

As a new method of one-class classification for mechanical fault diagnosis, support vector data description (SVDD) method is presented.

At last, a personal preview of further tasks in the research realms of neural network and support vector machine is presented.

Support vector machine is a machine learning approach which has powerful ability to captive verge point.

A classification algorithm of non-linear support vector machine (SVM) decision tree is presented.

A new geometric fast incremental learning algorithm for support vector machines (SVM) was proposed.

Support vector machine (SVM) and the neural network are both currently hot subject in the area of machine learning technology.

A novel method for fingerprint image segmentation using support vector machine was proposed.

According to the chaotic characteristic of power load, fuzzy support vector based kernel regression method is proposed for load forecasting.

The prediction results show that the prediction of traffic using the support vector machine (SVM) is feasible and effective.

LS_GSVMs' kernel functions have no or few limits when they are compared with standard support vector machines (SVMs).

The support vector machine (SVM) method is applied to forecast long-term run-off.

The fourth chapter makes use of the historical data and takes support vector machines to assort the credit condition of the tourism company.

Cleanness coefficient of the condenser is predicted based on support vector regression(SVR) time series prediction method.

Aiming at the problem of classification of Time-Varying Signal patterns, this paper presents a Process Support Vector Machine (PSVM) model.

The hyperplane revises the error of the standard Support Vector Machines.

Presents support vector machine to solve the classification problem based on the fire video and suspected fire video.

It is confirmed that the support vector machine theory can predict the velocity of blasting vibration well.

Compared to the univariate support vector regression, the MSVR method has a less complex structure and faster training speed and less work.

A time series forecasting method based on dynamic weighted least squares support vector machine (LSSVM) is proposed.

To solve the problem, an improved Local LSI (LLSI) method was proposed, using Support Vector Machine (SVM) to produce the local region.

Support vector machines(SVMs) are based on statistical learning theory( SLT ). It has been hot research in machine learning area.

Firstly, some critical issues about the nonlinear binary-class support vector machine (SVM) are studied.

Least square support vector machine (LS-SVM) is used to predict the trend of the aircraft flap system.

Then the input pattern of no-object classes could be rejected by the second support vector domain description(SVDD).

The high frequency signals and the low frequency are forecasted with least square support vector machines.

A new network optimization strategy of support vector regression (SVR) is presented.

A nonlinear model predictive control algorithm based on parallel support vector machines for multivariable systems is presented.

The uploaded Support Vector Machine (SVM) Classifier can classify text-type data well.

The paper mainly researches a traditional classifier: support vector machine (SVM) which is diffusely used in recent years.

Objective: This paper put forward for classifying cardiac arrhythmia signals based on ECG waveform features and support vector machine(SVM).

Support vector machine (SVM) is a novel pattern recognition technique.

Parameter selection of kernel function is an issue of nonlinear study based on Support Vector Machine (SVM).

Based on the theory of support vector machine (SVM), establishes a prediction model for building air conditioning load.

A method of classifying E-mail by Support Vector Machines (SVM) and Genetic Algorithm (GA) was proposed.

A method for selecting an optimal cost parameter C in support vector machine (SVM) is presented.

Furthermore, a hybrid system of Support Vector Machine (SVM) and Gaussian Mixed Model (GMM) is introduced.

A forecasting model for coal demand of China using a support vector regression was constructed.

However, the support vector machine, after all, a kind of new technology is not mature enough, there are many limitations.

This paper introduces a method to detect driver fatigue with support vector machine.

The experiment result proved that arithmetic of Support Vector Machine has the good effect in the text classification aspect.

Then, the classifier with multi-class support vector machines of DDAG (decision directed acyclic graph) is given.

So Support Vector Machines is introduced in the Automatic Grading of Tobacco Leaf in the paper.

A fault diagnosis technique based on support vector machines for cylinder and piston system in reciprocating compressor is put forward.

Support vector machine (SVM) is a powerful machine learning technique based on statistical learning theory ( SLT ).

A support vector machine (SVM) model is used to correlate the false alarm of BIT to time stress information.

Absrtact: SVM (support vector machine) algorithm is the newest branch of statistic learning theory.

The method uses RS to reduce original data to better reduce the dimension of support vector.

We emphases discussed the nearest neighbor classifier and support vector machine (SVM) based on the statistical study theory.

Another one is the intelligent forecasting model based on neural network, genetic algorithm, and support vector machine.

This paper presents an approach of rotor position estimation for switched reluctance motor based on support vector machine (SVM).

6 software control system operation simple and quick, support vector graphics software format.

This paper proposes a detection method based on support vector machine based on analysis and comparison with several other methods.

A novel approach to fault diagnosis of rolling bearings based on Weibull distribution model and support vector machine is proposed.

Under the rationale of the time series forecast, the article produced a model that based on the time series support vector machine forecast.

A novel approach to micro-displacement detection of dams based on Support Vector Machine and coherence average denoising is pro posed.

In this paper a method based on support vector machine is presented, which can discover prediction knowledge from historical alarms.

More over, Support Vector Machine is also proposed for classification of small samples.

Focus on inferior product in plastic blister tablets'packing, an inspection method based on Support Vector Machine is proposed.

Based on the support vector machine method, a motion control algorithm for the spherical robot is introduced.

Finally, the paper gave the detailed design of support vector machine forecasting method based on support vector machine.

This thesis analyzes forecasting methods and makes research of flight segment volume based on support vector machine.

judging the suspicion message by using the method of combination of Naive Bayesian classifier and support vector classifier.

It is difficult to apply the standard quadratic model optimization in the realization of support vector machine.

To deal with customer churn problem in textile industry, this paper set up prediction model based on support vector machine (SVM).

A method based on wavelet transform in combination with support vector machine (SVM) is proposed for localizing text region.

A new fitting method for magnetic curve based on support vector machine in transformer emulation is presented. The method is applied.

A structural damage identification method based on the support vector machine and the particle swarm algorithm was proposed.

A new image filter operator based on the least squares wavelet support vector machines (LS-WSVMs) is presented.

This paper sets up a new Support Vector Machine-Gussian Mixture Model(SVM-GMM) to improve speaker recognition rate based on open-set.

The support vector machine (SVM) is a new pattern recognition technology based on the statistical learning theory.

An approach for genotyping of STR locus based on ultraviolet(UV) spectroscopy and support vector machine(SVM) was studied.

A new Support Vector algorithm of classification ACNN-SVM is put forward aiming at the deficiency of NN-SVM.

A novel prediction model based on Least Squares Support Vector Machine (LS-SVM) with application in the milling area was proposed.

A phase space reconstructed forecasting method of stock price was proposed based on least squares support vector machines(LS-SVM).

A method for fatalness assessment of flight safety hidden danger, based on support vector machine(SVM), was proposed.

For the problem of automatic vehicle recognition, a recognition method is proposed based on EMD and support vector machine.

Relevance feedback algorithm based on support vector machine and rough set for image retrieval is approached.

A method is proposed to construct sparse least squares support vector machines (LSSVMs).

A novel model based on wavelet transform and support vector machine for dam deformation prediction is presented.

The paper presents an image denoising method based on Support Vector Clustering( SVC ) aiming at salt and pepper noise.

A novel aeroengine lubrication monitoring method based on support vector machines is presented in this paper.

When the number of the training set is very large, the training time and needed memory of support vector machine increase quickly.

According to extracted features, one support vector machine is constructed for one pattern.

In this paper, a forecast of the new model of inter-bank offered rate based on support vector machine model was made.

Support Vector Machines (SVM) is a novel algorithm of machine learning issued in 90's of last century.

A runoff prediction model was developed based on the multiple quantity index of SVM(support vector machine)method.

An evolutionary Support Vector Machines (SVM) algorithm based on time sequence was brought forward in the paper.

This paper proposes a thenar palmprint classification method based on Support Vector Machine(SVM).

A modeling method for nonlinear dynamic system based on Support Vector Regression (SVR) was proposed in this paper.

Rolling Bearing Status Monitoring method based on Support Vector Machines (SVM) is presented.

An error correction method for three axial fluxgate sensor based on support vector regression(SVR)is proposed.

This paper proposes a short term load forecasting (STLF) method by using clustering based support vector regression model.

In the character recognition module, a recognition approach based on support vector machine is applied.

An digital image watermarking algorithm based on support vector regression (SVR) is proposed in this paper.

SLS-SVRM obviously decreases the number of support vector without loss of the predicted accuracy.

Support Vector Machine (SVM) is a new data based modeling method based on statistical learning theory (SLT).

An audio steganalysis method for information detecting based on support vector machine is given.

A new algorithm to solve classification problems is obtained based on the support vector regression (SVR).

The paper proposes short-term traffic forecasting model based on support vector regression.

This paper presented a traffic sign recognition algorithm based on SVM(support vector machine).

A learning machine based on support vector machine is proposed in this paper, which is applied to single-chip smart sensor system.

Support vector machines (SVM) based on the statistical learning theory is a new machine learning tool.

An adaptive inverse control method based on Least Squares support vector machine(LS-SVM) is studied in this paper.

The support vector machine(SVM) is a linear classification machine, it is used commonly in the pattern recognition and nonlinear regression.

and performing identification based on a support vector machine.

A new time series prediction method was put forward, based on fuzzy least square support vector machine (FLS-SVM).

It makes use of support vector machines' strongly nonlinear mapping ability, and network's structure is optimally auto-created.

The support vector machine (SVM) is a new learning machine based on the statistical learning theory.

At the end, the definition of fuzzy support vector set is given, which can declare the characters of FSVM.

A laser welding quality monitoring method is proposed, based on support vector machine.

According to the analysis of the support vector machine theory, an improved SVM is presented based on one of quasi-Newton algorithms (DFP).

The technology of face recognition based on support vector machines is researched in this paper.

A method of rural electricity consumption demand forecasting based on support vector machine has been proposed.

The step of classification based on directed acyclic graph support vector machine.

Application of theory of support vector machine for forecasting link travel time is discussed.

Thus it can be seen, carries on the forecast prospect using the support vector machine to the stock market extremely to favor.

In order to verify the support vector machine for predicting the experience of timber stands of timber species was feasible.

A soft sensing modeling method based on Niche Genetic Algorithm-Least Square Support Vector Machine (NGA-LSSVM) is proposed.

Support Vector Machine (SVM) is a novel powerful machine learning method based on SLT.

A new method of dynamic clustering based on the idea of optimal hypersphere and support vector machine.

A new method for density estimation was developed based on the Support Vector Machine(SVM).

A method for signal filtering based on support vector machine (SVM) was proposed.

That's because we can combine active learning with support vector machines to improve the accuracy of image retrieval.

In addition, we combined SVM with rough set theory, and got rough support vector machine algorithm (RSVM).

Even for efficient algorithms such as Support Vector Machine, large-scale problems are still tough.

An approach based on Support Vector Machine(SVM) is presented in this paper.

Support Vector Machine in the field of data mining has become a hot one.

Results show that support vector regression is superior in learning, and make the converted voice more inclined to the target.

By using the C4. 5 and the support vector machine, the improved algorithm proposed in this paper achieved a higher recognition rate.

of computing resources based on support vector machines (SVM) was designed in order to ensure the systems stable running.

Support vector machine is a new learning machine, and it is based on the statistics learning theory.

A method based on one-against-one-algorithm multi-class classification support vector machine was proposed.

A new data fusion method based on one-class Support Vector Machine (SVM) was proposed for candidate gene prioritization.

Therefore it is of great significance to study the properties of kernel function of support vector machine.

Therefore, it is important to study the kernel function and fuzzy recognition technology based on support vector machine.

Study of Active Learning Support Vector Machine and Its Application on Mongolian Text Classification

Classification of Imaginary Hand Movements by Means of Continuous Wavelet Transform and Support Vector Machines

Improved Weighted Least Squares Support Vector Machines Algorithm and Its Applications in Spectroscopic Quantitative Analysis

A support vector machine with a hybrid kernel and its application in underwater target recognition

A Study of Loess Collapsibility by Combining Least Squares Support Vector Machines with Particle Swarm Optimization Algorithm

question answering; question classification; support vector machines; subset tree kernel; machine learning; natural language processing;

Semi-Supervised Support Vector Machines and Its Application to County Sustainable Development Strength Evaluation

Parameter tuning for Support Vector Regression with evolutionary strategy and its application in hot metal desulphurization

Application of a hybrid method in process monitor with independent component analysis and support vector machine

Support vector machine applied to the relationship between the content of some trace elements in hair and hypertension disease

Independent Component Analysis-Support Vector Regression and Its Application in Near Infrared Spectral Analysis

Research in Support Vector Machine And Its Application in Quickly Nondestructive Inspection of Agricultural Product

Principal Component Analysis-Support Vector Regression and Its Application In Near Infrared Spectral Analysis

A Study of Personal Credit Scoring Models on Support Vector Machine with Optimal Choice of Kernel Function Parameters

Support vector machine and mechanism model hybrid modeling method and its application to polyester production process

Quantitative Structure and Activity Relationship Studies on the Toxicity of Polychlorinated Naphthalenes Using Support Vector Machines

Overview of the support vector machines and its application research on equipment fault diagnosis

Application of the least square support vector machine in recognition of small samples

The Support Vector Machine Method and Its Elementary Applications to Synthetic Earthquake Prediction

Least Squares Wavelet Support Vector Machines and Its Application to Nonlinear System Identification

Linear Programming Support Vector Machine and Its Application in Underwater Target Recognition

Study of support vector machines for aeroengine performance trend forecasting

Support Vector Robust Perceptron for Linear Separable Problem and its Geometric Training Algorithm

A New Method of Wavelet and Support Vector Machine for Detection of the Train Wheel Bruise

Support vector regression applied to estimation of activity coefficients of some concentrated electrolyte solutions

Second order sufficient condition for optimization problem of a linear support vector classifier