Exploiting multiplex data relationships in Support Vector Machines

Mygdalis, Vasileios; Tefas, Anastasios; Pitas, Ioannis

doi:10.1016/j.patcog.2018.07.032

Cited by 13 publications

(7 citation statements)

References 29 publications

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“…Evaluation was performed with the proposed model based on actions performed by one subject and two subjects ("12 Actions"-column). Mygdalis et al [63] validated their model's performance using 3-fold cross-validation.…”

Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

A deep neural network model for multi-view human activity recognition

2022

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Multiple cameras are used to resolve occlusion problem that often occur in single-view human activity recognition. Based on the success of learning representation with deep neural networks (DNNs), recent works have proposed DNNs models to estimate human activity from multi-view inputs. However, currently available datasets are inadequate in training DNNs model to obtain high accuracy rate. Against such an issue, this study presents a DNNs model, trained by employing transfer learning and shared-weight techniques, to classify human activity from multiple cameras. The model comprised pre-trained convolutional neural networks (CNNs), attention layers, long short-term memory networks with residual learning (LSTMRes), and Softmax layers. The experimental results suggested that the proposed model could achieve a promising performance on challenging MVHAR datasets: IXMAS (97.27%) and i3DPost (96.87%). A competitive recognition rate was also observed in online classification.

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Section: Comparison With State-of-the-art Methodsmentioning

confidence: 99%

A deep neural network model for multi-view human activity recognition

2022

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“…One of the main advantages of the SVM classifier is that it automatically finds the support vectors for better classification [71]. Majorly, in every case the performance of SVM depends on the affected kernel function selection [72].…”

Section: Svmmentioning

confidence: 99%

A Review Study for Electrocardiogram Signal Classification

Abdulla

Al-Ani

2020

UHD J SCI TECH

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An electrocardiogram (ECG) signal is a recording of the electrical activity generated by the heart. The analysis of the ECG signal has been interested in more than a decade to build a model to make automatic ECG classification. The main goal of this work is to study and review an overview of utilizing the classification methods that have been recently used such as Artificial Neural Network, Convolution Neural Network (CNN), discrete wavelet transform, Support Vector Machine (SVM), and K-Nearest Neighbor. Efficient comparisons are shown in the result in terms of classification methods, features extraction technique, dataset, contribution, and some other aspects. The result also shows that the CNN has been most widely used for ECG classification as it can obtain a higher success rate than the rest of the classification approaches.

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“…The generalization ability of the learning machine is improved by seeking structural risk minimization. Moreover, the minimization of experience risk and confidence range is realized [22,23]. SVM is widely used in forecasting fields due to its high generalization ability, strong non-linear mapping ability and small sample size, such as photovoltaic power prediction, battery life prediction, insulated gate bipolar transistor (IGBT) life prediction and wind power prediction [24,25].…”

Section: Support Vector Machine (Svm) Regressionmentioning

confidence: 99%

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

Li²,

Lin

et al. 2019

Sustainability

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Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

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Exploiting multiplex data relationships in Support Vector Machines

Cited by 13 publications

References 29 publications

A deep neural network model for multi-view human activity recognition

A deep neural network model for multi-view human activity recognition

A Review Study for Electrocardiogram Signal Classification

Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

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