An approximate linear solver in least square support vector machine using randomized singular value decomposition

Liu, Bing; Xiang, Hua

doi:10.1007/s11859-015-1094-9

Cited by 4 publications

(4 citation statements)

References 16 publications

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“…4). The reason may be the hydroxyl groups of starch created hydrogen bonds and improved the hydrophilic ability of the polymer [13][14]. When the starch was less, the macromolecular network skeleton cannot be formed well and the hydroscopicity of the polymer will be poor, the starch was more, the hydrogen bond can be created between starch molecules.…”

Section: Results and Analysismentioning

confidence: 99%

The Synthesis and Properties of the Modified Bentonite-Starch Absorbent Polymer

Sun

Zhang

et al. 2013

AMR

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The composite absorbent polymer was synthesized by the solution polymerization method with acrylic acid, starch and bentonite as raw materials, N, N-methylene-bis-acrylamide as cross-linking agent, and ammonium persulphate as initiator. The whole process was under nitrogen protection. Factors including amount of starch, crosslinker, initiator, monomer, and neutralization degree and reaction temperature were investigated in terms of their influences on water absorbency. Effect of bentonite amount on polymer strength and water absorption rate was also tested. The modified polymer, with the composition of 10% corn starch, 0.15% initiator, 0.05% crosslinking agent and 8% bentonite, showed a significant strength enhancement at 65°C and 80% neutralization degree (molar ratio) with water absorption of 1012g/g in deionized water and 92g/g in 0.9% NaCl solution.

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Section: Results and Analysismentioning

confidence: 99%

The Synthesis and Properties of the Modified Bentonite-Starch Absorbent Polymer

Sun

Zhang

et al. 2013

AMR

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“…The disadvantage of this method is that the model training is performed twice, the solution process is complicated and time-consuming. Subsequently, an LSSVM algorithm with a fixed size sample set and a corresponding improved algorithm [3,4], and the method for combining LSSVM with other machine learning algorithms, have appeared [10,13]. The core concept of these algorithms is to compress large datasets into smaller sub-datasets, and then train them in the LSSVM model [15,16].…”

Section: Introductionmentioning

confidence: 99%

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Liu

2021

ITC

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Least squares support vector machine (LSSVM) is a machine learning algorithm based on statistical theory. Itsadvantages include robustness and calculation simplicity, and it has good performance in the data processingof small samples. The LSSVM model lacks sparsity and is unable to handle large-scale data problem, this articleproposes an LSSVM method based on mixture kernel learning and sparse samples. This algorithm reduces theinitial training set to a sub-dataset using a sparse selection strategy. It converts the single kernel function in theLSSVM model into a mixed kernel function and optimizes its parameters. The reduced sub-dataset is used fortraining LSSVM. Finally, a group of datasets in the UCI Machine Learning Repository were used to verify theeffectiveness of the proposed algorithm, which is applied to real-world power load data to achieve better fittingand improve the prediction accuracy.

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“…Suykens et al . have made substantial efforts using large‐scale data sets; these authors have developed many excellent algorithms [3–8] and combined the LSSVM with other algorithms for machine learning [9–11].…”

Section: Introductionmentioning

confidence: 99%

“…In the last ten years, in order to solve the sparse solution problem, a large number of scholars have conducted related research and proposed many new algorithms and improved algorithms to solve the training sample set sparsity problem. Suykens et al have made substantial efforts using large-scale data sets; these authors have developed many excellent algorithms [3][4][5][6][7][8] and combined the LSSVM with other algorithms for machine learning [9][10][11].…”

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confidence: 99%

Classification method based on the deep structure and least squares support vector machine

Liu

2020

Electron. lett.

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Support vector machines (SVMs) are one of the most representative shallow network models and have good generalisation abilities in small data sets. In this Letter, a new classification method based on the deep structure and least squares SVM (LSSVM) is proposed. For large-scale data sets, the method builds the structures of a multi-layer SVM. Using edge detection and the K-means algorithm, the sample set is compressed into a smaller sample set, which is used to train the LSSVM model of each layer and the discriminant classification function is obtained. Finally, this method is applied to UCI data sets and compared with several density-dependent quantised LSSVM methods and other methods. The experimental results show that the method has good performance in solving the large-scale data set classification problem.

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An approximate linear solver in least square support vector machine using randomized singular value decomposition

Cited by 4 publications

References 16 publications

The Synthesis and Properties of the Modified Bentonite-Starch Absorbent Polymer

The Synthesis and Properties of the Modified Bentonite-Starch Absorbent Polymer

Least Squares Support Vector Machine Regression Based on Sparse Samples and Mixture Kernel Learning

Classification method based on the deep structure and least squares support vector machine

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