Online training of support vector classifier

Lau, King Wai; Wu, Qiuxia

doi:10.1016/s0031-3203(03)00038-4

Cited by 104 publications

(55 citation statements)

References 10 publications

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“…(2) The prediction of model that fits the robust and nonparameter confidence interval is unknown. Lau et al [106] proposed an online support vector machine (SVM) learning algorithm to deal with the classification problem for sequentially provided input data. The classification algorithm is faster, with less support vectors, and has better generalization ability.…”

Section: Big Data Classificationmentioning

confidence: 99%

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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This paper attempts to identify the requirement and the development of machine learning-based mobile big data (MBD) analysis through discussing the insights of challenges in the mobile big data. Furthermore, it reviews the state-of-the-art applications of data analysis in the area of MBD. Firstly, we introduce the development of MBD. Secondly, the frequently applied data analysis methods are reviewed. Three typical applications of MBD analysis, namely, wireless channel modeling, human online and offline behavior analysis, and speech recognition in the Internet of Vehicles, are introduced, respectively. Finally, we summarize the main challenges and future development directions of mobile big data analysis.

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Section: Big Data Classificationmentioning

confidence: 99%

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

Xie

Song

et al. 2018

Wireless Communications and Mobile Computing

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“…30, we can see that the ULSSVC has a simpler method to get the classifier coefficients than the standard LSSVC. It's just the available approach for LSSVC to improve the efficiency of modelling, which will bring better performance for incremental learning (Shilton et al 2005;Vishwanathan et al 2006; and online learning (Lau and Wu 2003;Fan et al 2006;Suykens and Vandewalle. 2000;Cesa-Bianchi et al 2004).…”

Section: Fast Factorization Of the Unbiased Kernel Extended Matrixmentioning

confidence: 99%

An unbiased LSSVM model for classification and regression

et al. 2009

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Aiming at the common support vector machine's biased disadvantage and computational complexity, an unbiased least squares support vector machine (LSSVM) model is proposed in this paper. The model eliminates the bias item of LSSVM by improving the form of structure risk, then the unbiased least squares support vector classifier and the unbiased least squares support vector regression are deduced. Based on this model, we design a new learning algorithm using Cholesky factorization according to the characteristic of kernel function matrix, in this way the calculation of Lagrangian multipliers is greatly simplified. Several experiments on diffenert datasets are carried out, including the common datasets classification, synthetic aperture radar image automatic target recognition and chaotic time series prediction. The experimental results of correct recognition rate and the fitting precision testify that the unbiased LSSVM model has good universal ability and fitting accuracy, better generalization capability and stability, and have a great improvement in learning speed.

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“…But if learning samples are added to the training set along with time, the SVM's online learning algorithm must be used. Recent years, some researchers proposed many incremental [2][3][4] and online learning [5][6][7][8][9] methods based on standard SVM, the essence of such problems are still solutions of quadratic programming with restrictions, which has a high computational complexity. Then, some online learning algorithms based on LSSVM [10][11][12][13][14] appeared, as LSSVM saved much time through solving linear equations instead of quadratic programming.…”

Section: Introductionmentioning

confidence: 99%

A non-biased form of least squares support vector classifier and its fast online learning

Wang

Cai

Sun

2011

Neural Comput & Applic

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As an effective learning technique based on structural risk minimization, SVM has been confirmed an useful tool in many machine learning fields. With the increase in application requirement for some real-time cases, such as fast prediction and pattern recognition, the online learning based on SVM gradually becomes a focus. But the common SVM has disadvantages in classifier's bias and the computational complexity of online modeling, resulting in the reduction in classifier's generality and the low learning speed. Therefore, an non-biased least square support vector classifier(LSSVC) model is proposed in this paper by improving the form of structure risk. Also, a fast online learning algorithm using Cholesky factorization is designed based on this model according to the characteristic of the non-biased kernel extended matrix in the model's dynamic change process. In this way, the calculation of Lagrange multipliers is simplified, and the time of online learning is greatly reduced. Simulation results testify that the non-biased LSSVC has good universal applicability and better generalization capability, at the same time, the algorithm has a great improvement on learning speed.

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Online training of support vector classifier

Cited by 104 publications

References 10 publications

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

A Survey on Machine Learning‐Based Mobile Big Data Analysis: Challenges and Applications

An unbiased LSSVM model for classification and regression

A non-biased form of least squares support vector classifier and its fast online learning

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