Faster SVM training via conjugate SMO

Torres–Barrán, Alberto; Alaíz, Carlos M.; Dorronsoro, José R.

doi:10.1016/j.patcog.2020.107644

Cited by 22 publications

(8 citation statements)

References 12 publications

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“…In the equation, p-best and g-best represent the local and global optimal positions of the particle swarm, respectively. When c 1 � 0, it means that there is no ability to adjust according to its own optimal value, which is called the global PSO algorithm [10]. At this time, the particles have the ability to expand the search space.…”

Section: Classification Methods Of Network Ideological and Political Resources Based On Improved Svm Algorithm Optimizationmentioning

confidence: 99%

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

Wang

2021

Security and Communication Networks

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In order to improve the accuracy and efficiency of the classification of network ideological and political resources and promote the efficiency of ideological education, a research on the classification of network ideological and political resources based on the improved SVM algorithm is proposed. We analyze the characteristics and current situation of network ideological and political resources and conclude that the method elements are open and technical. The ontology elements are rich and shared, and the behavioral elements are autonomous and interactive. Three types of network ideological and political resources are proposed: the main resource, content resource, and means resource. The particle swarm algorithm is used to improve the SVM algorithm. In the process of constructing the SVM classifier, the fuzzy membership function is introduced, the classification problem of network ideological and political resources is converted into a secondary planning problem, and the accuracy of network ideological and political resources is finally realized. Simulation results show that the use of improved algorithms to classify network ideological and political resources can improve the accuracy and efficiency of network abnormal data classification.

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Section: Classification Methods Of Network Ideological and Political Resources Based On Improved Svm Algorithm Optimizationmentioning

confidence: 99%

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

Wang

2021

Security and Communication Networks

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“…However, there are certain challenges of applying SVM over large datasets. Targeting these challenges, ample research has been conducted in recent years [17], [18], [19], [20], [21].…”

Section: Svm For Large Datasetsmentioning

confidence: 99%

Random Partition Based Adaptive Distributed Kernelized SVM for Big Data

et al. 2022

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In this paper, we present a distributed classification technique for big data by efficiently using distributed storage architecture and data processing units of a cluster. While handling such large data, the existing approaches consider specific data partitioning techniques which demand complete data be processed before partitioning. This leads to an excessive overhead of high computation and data communication. The proposed method does not require any pre-structured data partitioning technique and is also adaptive to big data mining tools. We hypothesize that an effective aggregation of the information generated from data partitions by subprocesses of the complete learning process can lead to accurate prediction results while reducing the overall time complexity. We build three SVM based classifiers, namely one phase voting SVM (1PVSVM), two phase voting SVM (2PVSVM), and similarity based SVM (SIMSVM). Each of these classifiers utilizes the support vectors as the local information to construct the synthesized learner for efficiently reducing the training time and ensuring minimal communication between processing units. In this context, an extensive empirical analysis demonstrates the effectiveness of our classifiers when compared to other existing approaches on several benchmark datasets. However, among existing methods and three of our proposed (1PVSVM, 2PVSIM, and SIMSVM) methods, SIMSVM is the most efficient. Considering MNIST dataset, SIMSVM achieves an average speedup ratio of 0.78 and minimum scalability of 73% when the data size is scaled up to 10 times. It also retains high accuracy (99%) similar to centralized approaches.

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“…Applying the suggested methods, results are extracted from synthetic and real data, concluding that the use of such techniques brings good results that reduce the computational cost necessary for the execution of the model. Finally, Torres et al [41] improved a version of the SMO algorithm for training classification and regression SVMs, based on a Conjugate Descent procedure, decreasing the number of iterations needed for convergence. These cited methods are based on some sophisticated concepts to improve the computational performance, modifying some ideas of the basic theory of SVM.…”

Section: Svm Applied To Large Databasesmentioning

confidence: 99%

“…On the other hand, we observed that the method used in predicting students' performance is based on a shallow architecture and predictive result failure to capture the relationships among attributes in a massive data set, and a similar conclusion was already presented [57,58] and others in similar works. It is also worth mentioning the fact that the work developed is easily extendable for other contexts and methods, and there is also the possibility for parallelization adequacy, which guarantees an even greater computational time gain, or even a combination within other methodologies applied in large databases [37,41].…”

Section: Final Considerationsmentioning

confidence: 99%

Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

Pimentel

Ospina

Ara

2021

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The development of a country involves directly investing in the education of its citizens. Learning analytics/educational data mining (LA/EDM) allows access to big observational structured/unstructured data captured from educational settings and relies mostly on machine learning algorithms to extract useful information. Support vector regression (SVR) is a supervised statistical learning approach that allows modelling and predicts the performance tendency of students to direct strategic plans for the development of high-quality education. In Brazil, performance can be evaluated at the national level using the average grades of a student on their National High School Exams (ENEMs) based on their socioeconomic information and school records. In this paper, we focus on increasing the computational efficiency of SVR applied to ENEM for online requisitions. The results are based on an analysis of a massive data set composed of more than five million observations, and they also indicate computational learning time savings of more than 90%, as well as providing a prediction of performance that is compatible with traditional modeling.

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Faster SVM training via conjugate SMO

Cited by 22 publications

References 12 publications

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

A Classification Method of Network Ideological and Political Resources Using Improved SVM Algorithm

Random Partition Based Adaptive Distributed Kernelized SVM for Big Data

Learning Time Acceleration in Support Vector Regression: A Case Study in Educational Data Mining

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