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Evaluating machine learning models for clothing size prediction using anthropometric measurements from 3D body scanning. [PDF]
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Machine learning-based wind speed forecasting: a comparative study. [PDF]
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Applied Soft Computing, 2021
Abstract In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search).
Wencheng Huang +6 more
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Abstract In this paper, three algorithms are applied to obtain the parameters of Radial Basis Function (RBF) kernels of Support Vector Machines (SVM), which include: PSO (Particle Swarm Optimization), GA (Genetic Algorithm) and GS (Grid Search).
Wencheng Huang +6 more
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Proceedings of the 13th ACM Conference on Security and Privacy in Wireless and Mobile Networks, 2020
Connected vehicles leverage wireless interfaces to broadcast their motion state information for improved traffic safety and efficiency. It is crucial for their motion claims (location and velocity) to be verified at the receivers to detect spoofing attacks.
Mingshun Sun +3 more
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Connected vehicles leverage wireless interfaces to broadcast their motion state information for improved traffic safety and efficiency. It is crucial for their motion claims (location and velocity) to be verified at the receivers to detect spoofing attacks.
Mingshun Sun +3 more
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PB-SVM Ensemble: A SVM Ensemble Algorithm Based on SVM
Applied Mechanics and Materials, 2014As one of the most popular and effective classification algorithms, Support Vector Machine (SVM) has attracted much attention in recent years. Classifiers ensemble is a research direction in machine learning and statistics, it often gives a higher classification accuracy than the single classifier.
Bo Wang +3 more
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Proceedings of the 2019 2nd International Conference on Algorithms, Computing and Artificial Intelligence, 2019
Recent years witnesses the rampancy of telephone fraud along with the development of modern communication technology. The challenges from telephone fraud identification mainly exist in two aspects: (1) the telephone fraud records are typical imbalanced data due to the characteristic of heterogeneous spatial-temporal distribution, leading to bias ...
Qingqing Chang, Shaofu Lin, Xiliang Liu
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Recent years witnesses the rampancy of telephone fraud along with the development of modern communication technology. The challenges from telephone fraud identification mainly exist in two aspects: (1) the telephone fraud records are typical imbalanced data due to the characteristic of heterogeneous spatial-temporal distribution, leading to bias ...
Qingqing Chang, Shaofu Lin, Xiliang Liu
openaire +1 more source
Proceedings of the 25th international conference on Machine learning - ICML '08, 2008
We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.
Shai Shalev-Shwartz, Nathan Srebro
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We discuss how the runtime of SVM optimization should decrease as the size of the training data increases. We present theoretical and empirical results demonstrating how a simple subgradient descent approach indeed displays such behavior, at least for linear kernels.
Shai Shalev-Shwartz, Nathan Srebro
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Science in China Series G, 2004
Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ...
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Support vector machine (SVM), developed by Vapnik et al., is a new and promising technique for classification and regression and has been proved to be competitive with the best available learning machines in many applications. However, the classification speed of SVM is substantially slower than that of other techniques with similar generalization ...
openaire +1 more source