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Prestressed concrete components have high crack resistance and stiffness, but they may suffer damage and lead to major accidents under adverse environments and extreme loads. The study uses machine learning algorithms to construct an intelligent concrete damage recognition model aimed at accurately assessing its health status. The piezoelectric wave measurement method is used to collect small wave signals from concrete. The improved backpropagation network is used to identify concrete damage characteristics in the signals, and the support vector machine is taken to correct the identification results. According to the results, the mean square error, coefficient of determination, and F1 score of the damage location recognition model constructed by integrating two classification algorithms were 7.962×10-4, 0.9756, and 0.9836, respectively. For damage identification results, the mean square error, coefficient of determination, and F1 score of the research model were 6.548×10-2, 0.9531, and 0.9925, respectively. In the environment with introduced noise, the recognition accuracy of the research model was 93.7%. The results indicate that the research method has higher accuracy and robustness in damage identification compared with other models, which can be applied for concrete damage detection in large buildings or long-term high load buildings.

Support vector machine (SVM) is an optimal margin based classification technique in machine learning. SVM is a binary linear classifier which has been extended to non-linear data using Kernels and multi-class data using various techniques like one-versus-one, one-versus-rest, Crammer Singer SVM, Weston Watkins SVM and directed acyclic graph SVM (DAGSVM) etc. SVM with a linear Kernel is called linear SVM and one with a non-linear Kernel is called non-linear SVM. Linear SVM is an efficient technique for high dimensional data applications like document classification, word-sense disambiguation, drug design etc. because under such data applications, test accuracy of linear SVM is closer to non-linear SVM while its training is much faster than non-linear SVM. SVM is continuously evolving since its inception and researchers have proposed many problem formulations, solvers and strategies for solving SVM. Moreover, due to advancements in the technology, data has taken the form of ‘Big Data’ which have posed a challenge for Machine Learning to train a classifier on this large-scale data. In this paper, we have presented a review on evolution of linear support vector machine classification, its solvers, strategies to improve solvers, experimental results, current challenges and research directions.

Driver assistance systems have become a major safety feature of modern passenger vehicles. The advanced driver assistance system (ADAS) is one of the active safety systems to improve the vehicle control performance and, thus, the safety of the driver and the passengers. To use the ADAS for lane change control, rapid and correct detection of the driver’s intention is essential. This study proposes a novel preprocessing algorithm for the ADAS to improve the accuracy in classifying the driver’s intention for lane change by augmenting basic measurements from conventional on-board sensors. The information on the vehicle states and the road surface condition is augmented by using an artificial neural network (ANN) models, and the augmented information is fed to a support vector machine (SVM) to detect the driver’s intention with high accuracy. The feasibility of the developed algorithm was tested through driving simulator experiments. The results show that the classification accuracy for the driver’s intention can be improved by providing an SVM model with sufficient driving information augmented by using ANN models of vehicle dynamics.

Remote sensing technologies have been widely used in the contexts of land cover and land use. The image classification algorithms used in remote sensing are of paramount importance since the reliability of the result from remote sensing depends heavily on the classification accuracy. Parametric classifiers based on traditional statistics have successfully been used in remote sensing classification, but the accuracy is greatly impacted and rather constrained by the statistical distribution of the sensing data. To eliminate those constraints, new variants of support vector machine (SVM) are introduced. In this paper, we propose and implement land use classification based on improved SVM-enabled radial basis function (RBF) and SVM-Linear for image sensing. The proposed variants are applied for the cross-validation to determine how the optimization of parameters can affect the accuracy. The accuracy assessment includes both training and test sets, addressing the problems of overfitting and underfitting. Furthermore, it is not trivial to determine the generalization problem merely based on a training dataset. Thus, the improved SVM-RBF and SVM-Linear also demonstrate the outstanding generalization performance. The proposed SVM-RBF and SVM-Linear variants have been compared with the traditional algorithms (Maximum Likelihood Classifier (MLC) and Minimum Distance Classifier (MDC)), which are highly compatible with remote sensing images. Furthermore, the MLC and MDC are mathematically modeled and characterized with new features. Also, we compared the proposed improved SVM-RBF and SVM-Linear with the current state-of-the-art algorithms. Based on the results, it is confirmed that proposed variants have higher overall accuracy, reliability, and fault-tolerance than traditional as well as latest state-of-the-art algorithms.

Accurate monthly flow discharge prediction can yield significant evidence for sustainable management of water resources systems, optimal water allocation and use, mitigating flood events, and warning against famine. Inspiration to explore and develop skillful prediction models is a continuing attempt for various hydrologic assessments. The main aim of present study is to explore the potential of novel hybrid PSR-SVM-FFA model (integration of phase space reconstruction with support vector machine and firefly algorithm) and assess its performance against conventional radial basis function network, SVM, and hybrid SVM-FFA to predict flow discharge considering data from four gauge stations of Mahanadi River basin, India. PSR is applied to extract information and characteristics from flow time series and improve accuracy of hybrid SVM-FFA model. For assessing the model’s enactment, Nash–Sutcliffe coefficient root-mean-square error and Willmott’s Index (WI) indicators are calculated. The results showed that PSR-SVM-FFA model generated improved monthly flow predictions than other applied methods. The result indicates that best values of WI are 0.912–0.929, 0.949–0.956, 0.961–0.967, and 0.98–0.984 for RBFN, SVM, SVM-FFA, and PSR-SVM-FFA, respectively. This demonstrates that PSR-SVM-FFA provides prominent predictions compared to the other three approaches.

Support vector machines (SVMs) are versatile learning models which are used for both classification and regression. Several authors have reported successful applications of SVM in a wide range of fields. With the continuous growth and development in machine learning using SVM, it was observed that SVM also has some limitations. This paper focuses on limitation regarding its boundary, i.e., sensitivity to noise or outliers in the dataset. Researchers have proposed many variants and extensions of SVM to make it robust. This paper gives an overview of the developments in the field of robust statistics in support vector machines and its variants . This paper includes an up to date survey of the research development in the field of robustness in SVM and its extensions. It also includes a discussion part which not only discusses the pros and cons of the proposed approaches but also highlights some important future directions in it. This paper would be helpful for researchers working in the field of robust statistics as well as supervised machine learning. This study would also encourage the researchers to work further in the development of SVM and even its variants to improve them.

Customer churn prediction is very important for e-commerce enterprises to formulate effective customer retention measures and implement successful marketing strategies. According to the characteristics of longitudinal timelines and multidimensional data variables of B2C e-commerce customers’ shopping behaviors, this paper proposes a loss prediction model based on the combination of k-means customer segmentation and support vector machine (SVM) prediction. The method divides customers into three categories and determines the core customer groups. The support vector machine and logistic regression were compared to predict customer churn. The results show that each prediction index after customer segmentation was significantly improved, which proves that k-means clustering segmentation is necessary. The accuracy of the SVM prediction was higher than that of the logistic regression prediction. These research results have significance for customer relationship management of B2C e-commerce enterprises.

Support vector machines (SVMs) are a popular class of supervised learning algorithms, and are particularly applicable to large and high-dimensional classification problems. Like most machine learning methods for data classification and information retrieval, they require manually labeled data samples in the training stage. However, manual labeling is a time consuming and errorprone task. One possible solution to this issue is to exploit the large number of unlabeled samples that are easily accessible via the internet. This paper presents a novel active learning method for text categorization. The main objective of active learning is to reduce the labeling effort, without compromising the accuracy of classification, by intelligently selecting which samples should be labeled. The proposed method selects a batch of informative samples using the posterior probabilities provided by a set of multi-class SVM classifiers, and these samples are then manually labeled by an expert. Experimental results indicate that the proposed active learning method significantly reduces the labeling effort, while simultaneously enhancing the classification accuracy.

A histogram-valued random variable represents its value by a list of pairs of bins and their corresponding probabilities or relative frequencies. This type of data is a part of the symbolic data. There are many cases such as colors in image learning where histogram-valued data are naturally found. This study focuses on classification of the histogram-valued data by extending two approaches of support vector machine (SVM), namely, the expected-based and quantile-based probabilistic SVM on histogram-valued data. In both approaches, the cases of linear and nonlinear problems as well as the least-square classification are discussed. In addition, the extension to multi-class classification is also discussed. To compare the performance of the proposed procedures a simulation study has been done based on some generated data sets. The data are generated from various distributions with various parameters to represent different cases of classification, including binary and multi-class classification. Further, the methods are applied on two different real data sets. From the results, it can be concluded that our proposed methods perform well on wide range of classification problems.