SVM

This kernel is proposed in this paper and is proved to be able to improve training speed and precision of the wavelet SVM.

The biological experimental data used for candidate gene prioritization with one-class SVM is usually contaminated with errors and noise.

SVM, as a part of the system, is now can process online filtering towards email object.

Experimental results show that the constructed kernels used with an SVM classifier perform better than the existing sequence-based methods.

The purpose of this paper is to build up a detection model by using SVM-based fuzzy inference system for the impulse noise in an image.

The SVM algorithm is adopted for the traffic classification, since the minor one included in a combined traffic is generally small sampling.

Due to its complete theoretical basis as well as excellent performance, SVM has become a hotspot in the area of pattern recognition.

The application of a SVM ensures both the classification accuracy and the strong robustness of the system.

Continuous valued attributes were discretized by a linear SVM, the hyperplanes being used to locate the discrete points in attribute space.

Especially, SVM based relevance feedback greatly improve the performance of retrieval system with its good generalization.

This determination is carried out on-line for the coming new samples. This makes the solutions of LS-SVM having the feature of sparsity.

This system is composed of two SVM-based fuzzy inference subsystems and a decision subsystem.

The experiment showed that the accuracy and speed of the covering algorithm is better than that of SVM.

To this problem, this paper proposed one kind of method to choose the parameters of the SVM by particle swarm optimization algorithm (PSO).

The result shows the feedback learning can greatly improve the categorization performance of SVM and is an effective. . .

The SVM based color image restoration algorithm is extended and can be applied to restore sequential images.

The accuracy of classification of SVM in a two-class classification problem would be decreased because of those promiscuous samples.

SVM kernel function is one of the most critical factors that affect the recognition rate.

The experimental result shows that the SVM classifier can be applied in stratum recognition effectively, and then widely used in the future.

The key technology improves the system recognition rate is the SVM kernel function and parameter optimization.

As a new machine learning method, SVM can solve the small sample, nonlinear, high dimension and local minima, the actual problem.

Finally, a mathematic model for a typical mechanical position servo system with friction model by LS-SVM is established.

We proposed a sort of string kernels which derived from MM-SVM synthesis model.

In the modeling of structure, the hybrid kernel was applied, and the genetic algorithm was used to optimize the parameters of SVM.

In training a SVM, the solution of a quadratic programming (QP) problem constrains the SVM in large scale problem.

Recent enhancements to SVM include support for multiterabyte volumes, cluster volume manager, and thousands of partitions per physical disk.

SVM have been widely used in steganalysis, for its convenience and high performance.

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

A full-electric propulsion ship is chosen as a research object, and an improved SVM method is used to forecast its power load.

Experimental results show remarkable improvement on classification ability and classification speed of SVM, supporting this idea.

The proposed method has less number of parameter and is simple and reliable, comparing with SVM.

The work capitalized on public hand-classified network dataset and used it to train and tested the supervised SVM traffic classifier.

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.

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

The experimental results show that SVM method has higher recognition rate of urine sediment classification.

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

This paper improved the existing SVM algorithm with the granularity of the quotient space theory, proposed a new SVM algorithm (SVM-G).

At the bottom is a hardware platform that is virtualization capable (currently, this means an Intel VT or AMD-SVM processor).

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

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

Example calculation shows that the SVM method with consideration of the weather factor has high accuracy.

Whereas the relationship between SVM and reservoir history matching, the SVM is adopted to predict reservoir history matching parameters.

Because of the above advantages, SVM shows up greater adaptability and better performance among the algorithms of gene selection.

Then all the CCs' features are verified by 2-stage classification module which is composed by a cascade classifier and a SVM.

The model combines good time domain, frequency domain resolving ability of wavelet transformation and nonlinear learning ability of SVM.

Methods SVM regression estimate algorithm is applied to building forecasting model.

For incremental learning and large-scale learning problems, a fast incremental learning algorithm for the SVM was proposed.

Compared with BP Neural Network, the simulation result of predict shows that the presented method based on SVM is more accurate.

On this basis, SVM fault diagnosis models were established, and the performance of different models was compared.

The simulation experiments demonstrate that SVM can improve the accuracy of trajectory prediction while reduce the prediction time.

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

SVM provides us with a new method to develop the intelligent flow regimes identification.

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.

Conclusion SVM has excellent nonlinear generalization ability to high dimension and small sample evaluation problem.

Firstly, we analyzed the theory of Min-Max Modular network and its combination with SVM.

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

This paper introduces a new method which can judge the degree of burn scar hypertrophy by analyzing chroma of the burn scar.

Therefore, this research uses the SVM and the decision tree to forecast whether the customer will accept the promotion.

The combination of FCM and improved LDA is applied to SVM by multi models method, which can reinforce the algorithm.

Then, it uses LS-SVR to overcome the calculation slow and poor anti noise capability shortcomings of the conventional SVM.

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

Sphere-structured SVM of this algorithm can dispose unbalanced samples data because it considered the distribution of each class.

The results indicate that the performances of our methods are equal, sometimes superior, to FLD , KFD or SVM approaches.

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.

Firstly, the holes were identified and the LS-SVM model was trained by using the triangle vertices which gathered around the holes.

This paper presents a SVM classification method based on rough sets lower approximations theory and its application in continuous attribute.

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

This combination forecasting method, named SVM-GM, avoids over fitting problem and improves forecasting effect.

After using SVM, we find water quality of the middle reach of the reservoir is type II on the whole, but it is type III even IV in summer.

SVM makes minimizing of structure risk as its criteria, and gets both satisfying precision and great extending ability in data fitting.

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.

Kernel function is the key technology of SVM, the choice of kernel will affect the learning ability and generalization ability of SVM.

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

The SVM state database stores configuration and states information about volumes, including hot spares and disk sets.

A data pretreatment method, a kernel function and an a parameter choosing method for C-SVM were designed.

The sample-time-staggered space vector modulation (STS-SVM) is a novel modulation strategy with high performance.

The results showed that the SVM model is superior in predicting the financial bankruptcy risk to other methods.

Combining the IMC with the identification of inverse-model, the internal model controller of SVM (SVM-IMC) is proposed.

It was shown from the experimental results that FFT with SVM can achieve better results in the study of short-time sleep stages.

The cores of the two algorithms presented in this paper are to classify the samples with SVM method.

In this paper, we studied SVM as regression techniques for natural mineral water quality parameters prediction.

However, the classification accuracy of SVM is significantly affected by its training parameter.

Chaos optimization algorithm is global search method and it need not to consider SVM dimensionality and complexity.

SVM is a novel powerful machine learning method developed in the framework of Statistical Learning Theory (SLT).

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

Geological information at different depth was described by introducing a mixed window kernel function to SVM.

This file specifies whether the vmx (Intel) or svm (AMD) extensions are supported.

Though making SVM and BP neural network method act on the sample aggregate respectively to locate, and quantify damage.

The conclusion shows that SVM algorithm has well research value and applied foreground in the area of gas classification.

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

So, designing a effective SVM training method is becoming a important content of the research of SVM.

Then, a decision-making method for multi-class SVMs is proposed to recombine the outputs of several binary classifiers.

Experimental results show that the forecast effect of PLS-SVM model precedes obviously the common PLS model.

SVM is a novel learning method that has solid theoretical basis and requires only small amount of sample.

And then discussed in detail based on the SVM network in the area nearby out of control ship trajectory prediction model.

SVM data fusion approach for GPS and radar system's joint observation of maneuvering target tracking was presented.

The experimental work shows that the KCCA based classier outperform SVM, especially when the embedding rate is low.

Experimental results show that using SVM-GMM model can improve the open-set speaker recognition rate effectively.

Besides, RVM doesn't need turning any parameters of model, and has not any restriction on the selection of kernel function.

First, structural principle and basic theory of SVM are analyzed and researched in this paper.

When SVM resolve problems about small-sample set, non-linear and high dimensions, SVM has especial advantages.

In this control structure, an SVM is used as identifier, the control signal is solved by an exponentiated gradient algorithm.

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

A multiple SVM classification is designed to distinguish the intermittent failure from the normal and the hard failure.

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.

Of course SVM is not really just paint and mark regions, but with the example above you should get some idea about what SVM is doing.

It also discusses the primary processing and recognition diagram of SVM applied to face recognition.

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

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

Imagery classification method of SVM is tested to landslide interpretation, so the situation of landslides is determined.

The application of SVM regression method on Zhejiang drought and flood's forecast at flood season was studied.

Both SVM forecast models perform satisfactorily and can refer references to real business.

This paper puts forth a set of damage identification technique based on SVM and dynamic flexibility matrix.

Then, set the elements of each feature vector of SVM according to features of text layout, words and contents, etc.

And in the frame of SVM, some rules are introduced to complement the shortage of statistics-based method to improve the performance.

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

The two-dimensional SVM control strategy can still be available in four-leg three phase inverter.

This paper presents an SVM based method using color and shape information for detection of traffic signs.

Finally, it proposes a class of NN algorithm development from the angle of core idea of SVM algorithm.

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

The SVM classifier's low error rate of classification offers a better base for the trust region algorithm.

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

Soft sensor model based on LS SVM is applied to predication of frozen point of light diesel oil.

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

SVM provides you with the ability to manage large number of disks, and it also improves data availability.

The nonlinear relation between sand liquefactions and influencing factors was learned from the finite empirical data by SVM model.

However, boosting- or SVM-based methods will require more training images to get good results.

However, SVM requires to solve a quadratic optimization problem and its training time is about quadratic to the number training samples.

Firstly, the algorithm of SVM in regression approximation is introduced.

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.

Based on wavelet analysis of signal characteristics, distributed multi-SVM classifier is utilized to identify the power supply line fault.

To improve the modeling speed of LS-SVM, a recursive algorithm of increased memory mode for VBL algorithm is also proposed.

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.

With the introduction of mixed kernels, SVM has one more adjustable parameter.

At last, contrastive experiment of FSVM classification and traditional SVM classification is given.

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

In this article, the focus is the hardware-based implementation and optimization for SVM.

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.

The simulation results show that the data predicted by SVM is satisfying.

An optimal subset of features is selected as input vector of SVM for training and recognition.

Then, the approximately linear SVM is obtained by solving the dual problem of maximal margin method.

Simple SVM can only handle binary classification problems; can not directly handle multi-value classification.

A multiclass classification method of SVM is applied to lake water quality assessment.

Since the analysis is from two viewpoints, it can cover and distinguish most of the existing SVMs.

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

It is obvious that application of SVM in time series prediction of electric current will be of great significance.

From the experiment, it is preyed that the SVM has better generalization ability under the conditions of limited samples.

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.

But as a new technology, SVM, which still has some limitations.

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

The dimension reduction of hyperspectral data was classified by SVM.

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

In the end, for the extracted features, we used PCA for dimension reduction and SVM for recognition.

However, as a kind of supervised classifier, SVM can't automatically extract feature in the image.

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

Firstly, reduced the dimensions of correlated inputs and denoised for sample by PLS, then construct the SVM model.

Secondly, it brings up a structural damage identification method based on SVM.

BVM(Ball Vector Machine)is a faster machine learning algorithm than SVM.

The classification capability of SVM to is not affected because the border vectors contain support vectors.

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.

Besides, voice language identification results show that PONN method based on barycenter rule outperforms SVM under certain circumstances.

Based on the study of the classification scores of the linear SVM classifiers, we introduce a method to handle the occlusion.

And, the fault diagnosis method based on SVM with feature selection can reliably diagnose the faults online in the complex chemical process.

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.

In chapter two, the dissertation described detailedly the application of HM and RBFNN and SVM in chemistry and environmental chemistry.

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.

Secondly, set each content body a SVM in charge of content recognition.

Methods Principal component analysis and Factor analysis, BPNN, SVM.

The results show that the prediction model of chaotic time series based on wavelet de-noising and LS-SVM is better.

Because the structural risk minimization principle makes SVM exhibit good generalization.

At last according to feature information of audio signals, classification is realized by SVM theory.

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

The basic idea of the new model is that the classification results of the SVM are confirmed with GMM.

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).

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

Then, an improved multi-class SVM based on ensemble learning is constructed to classify and recognize these ROIs.

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

The human face for each frame was detected by the algorithm based on skin color and SVM.

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

So an improvement SVM learning algorithm is proposed based on circular iterative algorithm.

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

Now, SLT and SVM have become new hotspots in the area of machine learning.

Thus, a feature extraction method based on SVM was proposed to select the LSI features fit for classification.

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

Volumes in SVM are built from slices (disk partitions).

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

A min max method for selecting the parameters of SVM is presented.

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

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

Verified code recognition based on SVM is widely used in e-commerce.

Introduce a novel speake identification system based on GMM and SVM, with feature extract, algorithm research and experiment data.

The multi-class SVM has recently been extensively applied in the field of pattern recognition.

Because the training assemble are tremendous and non-linear, a concept called sub-clusters reduced SVM is presented.

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

On condition that adopting the same spectrum, the discriminant model using SVM method was superior than the one established by DPLS.

Then a pre trained SVM is used to classify text and non-text and the text region are located.

In the SVM-based speaker recognition system study, it is important to obtain a ideal recognition rate.

Then a pre- trained SVM is used to classify character and non character.

The SVM saves all information about structures of volumes on a disk area called the replica.

At last, SVM is applying to image classification.

Finally, based on the above theory and using C , the SVM algorithm is presented to realize a verification code identification system.

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

The results showed that the LS-SVM model. Performed not only a quick study speed, but also a good predictable precision and stability.

In dipeptide based prediction experiments of enzyme thermostability, the accurary when polar of amino acid was feature vector was 71. 9%.

In this paper, a text classification model with positive examples, is brought forward based on SVM.

Secondly, The principle of intelligent decision making based on SVM is discussed.

Detailedly deduced the training and decision-making process of SVM from linear SVM to Non-linear SVM and sum the training algorithm. 2.

A SVM model based on the posterior probability is bring forward by using a commercial bank's housing credit data.

Then, this thesis introduces some algorithms of text classification, including SVM, KNN, Naive Bayes and so on.

Secondly, introduce a feature selection method based on the weight of normal from SVM model.

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

A novel direct torque control scheme based on space vector modulation (SVM) is proposed.

analyzing the characteristics of SVM and Bayes in spam mail filtering. 2.

To mirror and use LVM without VERITAS, you would have to bring these filesystems under the control of SVM.

A new direct torque and flux control based on space-vector modulation(DTC-SVM) for induction motor sensorless drives is introduced.

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.

When this is complete, identify slices that are available for use by the SVM.

A new method of condition trend prediction based on SVM for mechanical equipment is put forward.

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

Then, we design and implement an audio classification module based on SVM.

Knows how to branch and merge, use patches setup repository properties etc. Knowledge of distributed VCS systems.

In this paper, a new method of license plate location and extraction based on edge and SVM is presented.

Firstly, we use the algorithm based on face segmentation and SVM to detect the faces.

The model introduced genetic algorism into SVM, so it can adaptively adjust kernel parameters of SVM and obtain exact image information.

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

This method is compared with SVM model selection method based on GA method.

The maximum predicted error of least squares SVM was 7. 12%, with an average error of 4. 81%.

So we can consider SVM as a black box. Just push data into SVM and use the output data.

A gear defect recognition model was built based on the SVM.

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