Quadratic optimization fine tuning for the Support Vector Machines learning phase

González-Mendoza, Miguel; Orozco, Rodolfo E. Ibarra; Gamboa, Ariel L. García; Hernández-Gress, Neil; Mora-Vargas, Jaime; López-Pimentel, Juan Carlos

doi:10.1016/j.eswa.2013.08.019

Cited by 4 publications

(4 citation statements)

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“…Ding [19] suggested the FVI-SVM algorithm, which narrowed the volume of the training data set using a KKT (Karush-Kuhn-Tucker) condition and improved classification efficiency. Gonzalez-Mendoza [20] introduced KKT conditions and considered the KKT optimality conditions in order to present a strategy to implement the SVM-QP (quadratic optimization problem based on Support Vector) Machines. Among those studied, the BatchSVM incremental learning algorithm could continually accumulate more support vector sets, but when data endlessly increased, it could bring too great a burden to perform the computations.…”

Section: Related Work and Problem Analysis 21 Related Workmentioning

confidence: 99%

“…Moreover, data stream, which is real-time continuous, will pose a heavy burden on computation. An incremental learning algorithm based on a KKT condition [20] will also decrease the sample amount in incremental samples, which will take part in the next step of training. This lowers the classification accuracy because a lot of incremental samples are filtered at the same time.…”

Section: Problem Analysismentioning

confidence: 99%

“…Based on the analysis of section 2.2, aiming to resolve the issues of BatchSVM [18] and KKT incremental algorithm [20], which are the problem of reserving valid history data and the problem of imbalanced data volumes in the history sample set and the incremental sample set, a data-stream-oriented improved incremental learning algorithm of SVM, named the IILS, is described below:…”

Section: Iils Algorithm Designmentioning

confidence: 99%

“…The IILS algorithm is the core of the DS-IILS algorithm in this experiment, i.e., the IILS is a subfunction of the DS-IILS and an exception of the DS-IILS when the number of cloud nodes is one. In the experimental comparisons explored during this research, we compared the DS-IILS with the standard SVM [9], the BatchSVM incremental algorithm [18], and the KKT incremental algorithm [20], thereby excluding the IILS. Again, the focus of our comparisons includes training time and accuracy in order to determine the effectiveness of the DS-IILS algorithm.…”

Section: Experimental Comparisonsmentioning

confidence: 99%

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SVM-Based Incremental Learning Algorithm for Large-Scale Data Stream in Cloud Computing

Wang¹,

Yang²,

Feng³

et al. 2014

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We have witnessed the rapid development of information technology in recent years. One of the key phenomena is the fast, near-exponential increase of data. Consequently, most of the traditional data classification methods fail to meet the dynamic and real-time demands of today's data processing and analyzing needs--especially for continuous data streams. This paper proposes an improved incremental learning algorithm for a large-scale data stream, which is based on SVM (Support Vector Machine) and is named DS-IILS. The DS-IILS takes the load condition of the entire system and the node performance into consideration to improve efficiency. The threshold of the distance to the optimal separating hyperplane is given in the DS-IILS algorithm. The samples of the history sample set and the incremental sample set that are within the scope of the threshold are all reserved. These reserved samples are treated as the training sample set. To design a more accurate classifier, the effects of the data volumes of the history sample set and the incremental sample set are handled by weighted processing. Finally, the algorithm is implemented in a cloud computing system and is applied to study user behaviors. The results of the experiment are provided and compared with other incremental learning algorithms. The results show that the DS-IILS can improve training efficiency and guarantee relatively high classification accuracy at the same time, which is consistent with the theoretical analysis.

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Section: Related Work and Problem Analysis 21 Related Workmentioning

confidence: 99%

Section: Problem Analysismentioning

confidence: 99%