Optimal reduction of solutions for support vector machines

Lin, Hwei-Jen; Yeh, Jih Pin

doi:10.1016/j.amc.2009.04.010

Cited by 38 publications

(25 citation statements)

References 5 publications

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“…It should be noted that we used, during the filtering, a tube width σ = 3. It is important to note also that the proposed filtering uses the same kernel of learning and the RBF kernel used here is chosen only to compare the results to those obtained in Liu and Feng (2009) and Lin and Yeh (2009). The evaluating of the performances of our methods is based on n the number of samples kept after filtering, T(s) the learning time in seconds based on n samples and R the recognition rate.…”

Section: Implementation and Evaluation Criteriamentioning

confidence: 99%

“…In Lin and Yeh (2009), authors propose a reduction method based on genetic algorithms with elitism. They start from a population of randomly chosen solutions, and use an appropriate fitness function to achieve the optimal set of support vectors.…”

Section: Related Workmentioning

confidence: 99%

“…Then, we tested it on benchmark databases of binary classification on the site 'machine learning repository UCI' (Lin and Yeh, 2009). The used databases are shown in Table 1.…”

Section: Used Datasetsmentioning

confidence: 99%

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Fast binary support vector machine learning method by samples reduction

Djeffal

Babahenini

Taleb‐Ahmed

2017

IJDMMM

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Support vector machine is a well-known method of statistical learning by its good accuracy; however, its training time is very poor especially in case of huge databases. Many research works aim to reduce training samples to improve training time without significant loss in accuracy. In this paper, we propose a method called CB-SR, based on filtering and revision stages to eliminate samples that have little influence on learning results. Filtering stage uses a covering-based principle of samples to eliminate those faraway from decision boundaries and keep the closest ones. Revision stage allows to add after the first learning, samples eventually discarded by mistake. The results we obtain show the benefits of our approach over others existing ones.Keywords: support vector machine; binary SVM; samples reduction; fast training; support vectors; separating hyperplane; separating margin; decision boundaries; statistical learning; machine learning.Reference to this paper should be made as follows: Djeffal, A., Babahenini, M.C. and Taleb-Ahmed, A. (2017)

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Section: Implementation and Evaluation Criteriamentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Fast binary support vector machine learning method by samples reduction

Djeffal

Babahenini

Taleb‐Ahmed

2017

IJDMMM

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“…In SVM there is the need to use Kernel Functions (Lin and Yeh, 2009). For this study, Kernel radial basic function (RBF) and regression sequential minimal optimization algorithm were used.…”

Section: Support Vector Machine (Svm)mentioning

confidence: 99%

Reference evapotranspiration in So Paulo State: Empirical methods and machine learning techniques

Tangune¹,

Escobedo²

2018

Int. J. Water Res. Environ. Eng.

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“…SVMs have the ability to enable a learning machine to generalize well to unseen data with their strong statistical learning theory grasp and very promising in empirical performance (Lin & Yeh 2009). There are a wide number of applications that can be utilized by using SVMs such as regression, pattern recognition, Bioinformatics and artificial intelligence (Tripathi et al 2006).…”

Section: Introductionmentioning

confidence: 99%

Atterberg Limits Prediction Comparing SVM with ANFIS Model

Sherzoy

2017

J. Geoscience Eng. Environ. Technol.

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Support Vector Machine (SVM) and Adaptive Neuro-Fuzzy inference Systems (ANFIS) both analytical methods are used to predict the values of Atterberg limits, such as the liquid limit, plastic limit and plasticity index. The main objective of this study is to make a comparison between both forecasts (SVM & ANFIS) methods. All data of 54 soil samples are used and taken from the area of Peninsular Malaysian and tested for different parameters containing liquid limit, plastic limit, plasticity index and grain size distribution and were. The input parameter used in for this case are the fraction of grain size distribution which are the percentage of silt, clay and sand. The actual and predicted values of Atterberg limit which obtained from the SVM and ANFIS models are compared by using the correlation coefficient R 2 and root mean squared error (RMSE) value. The outcome of the study show that the ANFIS model shows higher accuracy than SVM model for the liquid limit (R 2 = 0.987), plastic limit (R 2 = 0.949) and plastic index (R 2 = 0966). RMSE value that obtained for both methods have shown that the ANFIS model has represent the best performance than SVM model to predict the Atterberg Limits as a whole.

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Optimal reduction of solutions for support vector machines

Cited by 38 publications

References 5 publications

Fast binary support vector machine learning method by samples reduction

Fast binary support vector machine learning method by samples reduction

Reference evapotranspiration in So Paulo State: Empirical methods and machine learning techniques

Atterberg Limits Prediction Comparing SVM with ANFIS Model

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