A comprehensive review on the variants of support vector machines

2019 ◽  
Vol 33 (25) ◽  
pp. 1950303 ◽  
Author(s):  
Bagesh Kumar ◽  
O. P. Vyas ◽  
Ranjana Vyas
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Model Selection ◽  
Support Vector ◽  
Automated Extraction ◽  
Future Directions ◽  
Comprehensive Review ◽  
Vector Machines ◽  
Unsupervised Approaches ◽  
Inputs And Outputs

Machine learning (ML) represents the automated extraction of models (or patterns) from data. All ML techniques start with data. These data describe the desired relationship between the ML model inputs and outputs, the latter of which may be implicit for unsupervised approaches. Equivalently, these data encode the requirements we wish to be embodied in our ML model. Thereafter, the model selection comes in action, to select an efficient ML model. In this paper, we have focused on various ML models which are the extensions of the well-known ML model, i.e. Support vector machines (SVMs). The main objective of this paper is to compare the existing ML models with the variants of SVM. Limitations of the existing techniques including the variants of SVM are then drawn. Finally, future directions are presented.

scholarly journals Research on Parallel Support Vector Machine Based on Spark Big Data Platform

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Yao Huimin
Keyword(s):  
Machine Learning ◽  
Support Vector Machine ◽  
Big Data ◽  
Support Vector Machines ◽  
Cross Validation ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Lambda Architecture ◽  
Vector Machines ◽  
Data Platform

With the development of cloud computing and distributed cluster technology, the concept of big data has been expanded and extended in terms of capacity and value, and machine learning technology has also received unprecedented attention in recent years. Traditional machine learning algorithms cannot solve the problem of effective parallelization, so a parallelization support vector machine based on Spark big data platform is proposed. Firstly, the big data platform is designed with Lambda architecture, which is divided into three layers: Batch Layer, Serving Layer, and Speed Layer. Secondly, in order to improve the training efficiency of support vector machines on large-scale data, when merging two support vector machines, the “special points” other than support vectors are considered, that is, the points where the nonsupport vectors in one subset violate the training results of the other subset, and a cross-validation merging algorithm is proposed. Then, a parallelized support vector machine based on cross-validation is proposed, and the parallelization process of the support vector machine is realized on the Spark platform. Finally, experiments on different datasets verify the effectiveness and stability of the proposed method. Experimental results show that the proposed parallelized support vector machine has outstanding performance in speed-up ratio, training time, and prediction accuracy.

scholarly journals Impacts of multicollinearity on CAPT modalities: An heterogeneous machine learning framework for computer-assisted French phoneme pronunciation training

PLoS ONE ◽  
2021 ◽  
Vol 16 (10) ◽  
pp. e0257901
Author(s):  
Yanjing Bi ◽  
Chao Li ◽  
Yannick Benezeth ◽  
Fan Yang
Keyword(s):  
Machine Learning ◽  
Neural Networks ◽  
Support Vector Machines ◽  
Partial Least Square ◽  
Least Square ◽  
Support Vector ◽  
Computer Assisted ◽  
Long Distance ◽  
Relationship Analysis ◽  
Vector Machines

Phoneme pronunciations are usually considered as basic skills for learning a foreign language. Practicing the pronunciations in a computer-assisted way is helpful in a self-directed or long-distance learning environment. Recent researches indicate that machine learning is a promising method to build high-performance computer-assisted pronunciation training modalities. Many data-driven classifying models, such as support vector machines, back-propagation networks, deep neural networks and convolutional neural networks, are increasingly widely used for it. Yet, the acoustic waveforms of phoneme are essentially modulated from the base vibrations of vocal cords, and this fact somehow makes the predictors collinear, distorting the classifying models. A commonly-used solution to address this issue is to suppressing the collinearity of predictors via partial least square regressing algorithm. It allows to obtain high-quality predictor weighting results via predictor relationship analysis. However, as a linear regressor, the classifiers of this type possess very simple topology structures, constraining the universality of the regressors. For this issue, this paper presents an heterogeneous phoneme recognition framework which can further benefit the phoneme pronunciation diagnostic tasks by combining the partial least square with support vector machines. A French phoneme data set containing 4830 samples is established for the evaluation experiments. The experiments of this paper demonstrates that the new method improves the accuracy performance of the phoneme classifiers by 0.21 − 8.47% comparing to state-of-the-arts with different data training data density.

Combination with Machine Learning Algorithms for the Classification in E-Bussiness

2011 ◽  
Vol 230-232 ◽  
pp. 625-628
Author(s):  
Lei Shi ◽  
Xin Ming Ma ◽  
Xiao Hong Hu
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Set Theory ◽  
Rough Set ◽  
Rough Set Theory ◽  
Machine Learning Algorithms ◽  
Classification Model ◽  
Support Vector ◽  
Mathematical Tool ◽  
Vector Machines

E-bussiness has grown rapidly in the last decade and massive amount of data on customer purchases, browsing pattern and preferences has been generated. Classification of electronic data plays a pivotal role to mine the valuable information and thus has become one of the most important applications of E-bussiness. Support Vector Machines are popular and powerful machine learning techniques, and they offer state-of-the-art performance. Rough set theory is a formal mathematical tool to deal with incomplete or imprecise information and one of its important applications is feature selection. In this paper, rough set theory and support vector machines are combined to construct a classification model to classify the data of E-bussiness effectively.

scholarly journals Linear Support Vector Machines for Prediction of Student Performance in School-Based Education

2020 ◽  
Vol 2020 ◽  
pp. 1-7
Author(s):  
Nalindren Naicker ◽  
Timothy Adeliyi ◽  
Jeanette Wing
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Student Performance ◽  
State Of The Art ◽  
Learning Algorithms ◽  
The State ◽  
Machine Learning Algorithms ◽  
Superior Performance ◽  
Support Vector ◽  
Vector Machines

Educational Data Mining (EDM) is a rich research field in computer science. Tools and techniques in EDM are useful to predict student performance which gives practitioners useful insights to develop appropriate intervention strategies to improve pass rates and increase retention. The performance of the state-of-the-art machine learning classifiers is very much dependent on the task at hand. Investigating support vector machines has been used extensively in classification problems; however, the extant of literature shows a gap in the application of linear support vector machines as a predictor of student performance. The aim of this study was to compare the performance of linear support vector machines with the performance of the state-of-the-art classical machine learning algorithms in order to determine the algorithm that would improve prediction of student performance. In this quantitative study, an experimental research design was used. Experiments were set up using feature selection on a publicly available dataset of 1000 alpha-numeric student records. Linear support vector machines benchmarked with ten categorical machine learning algorithms showed superior performance in predicting student performance. The results of this research showed that features like race, gender, and lunch influence performance in mathematics whilst access to lunch was the primary factor which influences reading and writing performance.

scholarly journals Asymptotic Behaviors of Support Vector Machines with Gaussian Kernel

2003 ◽  
Vol 15 (7) ◽  
pp. 1667-1689 ◽  
Author(s):  
S. Sathiya Keerthi ◽  
Chih-Jen Lin
Keyword(s):  
Support Vector Machines ◽  
Model Selection ◽  
Heuristic Method ◽  
Gaussian Kernel ◽  
Support Vector ◽  
Svm Classifier ◽  
Vector Machines ◽  
Rbf Kernel ◽  
Linear Svm ◽  
Generalization Errors

Support vector machines (SVMs) with the gaussian (RBF) kernel have been popular for practical use. Model selection in this class of SVMs involves two hyper parameters: the penalty parameter C and the kernel width σ. This letter analyzes the behavior of the SVM classifier when these hyper parameters take very small or very large values. Our results help in understanding the hyperparameter space that leads to an efficient heuristic method of searching for hyperparameter values with small generalization errors. The analysis also indicates that if complete model selection using the gaussian kernel has been conducted, there is no need to consider linear SVM.

scholarly journals Automatic pathology classification using a single feature machine learning support - vector machines

2014 ◽  
Author(s):  
Fernando Yepes-Calderon ◽  
Fabian Pedregosa ◽  
Bertrand Thirion ◽  
Yalin Wang ◽  
Natasha Lepore
Keyword(s):  
Machine Learning ◽  
Support Vector Machines ◽  
Support Vector ◽  
Learning Support ◽  
Pathology Classification ◽  
Single Feature ◽  
Vector Machines
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