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How to use tune SVM in R?

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Structured support vector machine

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Proceedings Article•DOI An algorithm of soft fault diagnosis for analog circuit based on the optimized SVM by GA Hua Li, YongXin Zhang - Show less +1 more 02 Oct 2009 40 Citations	The selection of SVM parameters has an important influence on the classification accuracy of SVM.
Proceedings Article•DOI Hybrid optimization method for parameter selection of support vector machine Xiao Huai-tie, Feng Guoyu, Song Zhiyong, Chen Jianjun - Show less +3 more 06 Dec 2010 7 Citations	The experiment results demonstrate that, compared with cross validation method, this proposed method improves accuracy of SVM parameter selection and generalization performance of SVM.
Proceedings Article•DOI Stock forecasting using Support Vector Machine Lucas K. C. Lai, James N. K. Liu - Show less +1 more 11 Jul 2010 22 Citations	SVM performs well in the short term forecast.
Open access•Journal Article•DOI Quantum-inspired evolutionary tuning of SVM parameters Zhiyong Luo, Ping Wang, Yinguo Li, Wenfeng Zhang, Wei Tang, Min Xiang - Show less +5 more 10 Apr 2008-Progress in Natural Science 29 Citations	The simulation results show that the proposed approach can effectively tune the parameters of LS-SVM, and the improved LS-SVM with wavelet kernel can provide better precision.
Open access•Journal Article•DOI Ensemble SVM Method for Automatic Sleep Stage Classification Emina Alickovic, Abdulhamit Subasi - Show less +1 more 15 Feb 2018-IEEE Transactions on Instrumentation and Measurement 196 Citations	The proposed classifier combines advantages of the principal component analysis and SVM to improve classification performances of the traditional SVM.
Journal Article•DOI A Multisampling SVM Scheme for Current Source Converters With Superior Harmonic Performance Jingya Dai, Yongqiang Lang, Bin Wu, Dewei Xu, Navid R. Zargari - Show less +4 more 21 Aug 2009-IEEE Transactions on Power Electronics 53 Citations	The proposed MS-SVM method exhibits a superior low-order harmonic performance comparable to that of SHE and provides the same fast and flexible control capability as the conventional SVM.
Open access•Proceedings Article•DOI GA-SVM Based Framework for Time Series Forecasting Thi H. O. Nguyen, Lee Gordon-Brown, Peter Wheeler, James Andrew Peterson - Show less +3 more 14 Aug 2009 10 Citations	Experiments demonstrated that the integrated GA-SVM approach is superior compared to conventional SVM applications.

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Does Relevance Vector Machine (RVM) model have hyperparameter tuning issues?5 answersThe Relevance Vector Machine (RVM) model does have hyperparameter tuning issues. Several papers discuss the importance of selecting the appropriate hyperparameters for RVM models.. These papers propose various methods to address this issue, including grid search, random search, Bayesian optimization, simulated annealing, particle swarm optimization, and evolutionary algorithms.. They compare the performance of different search algorithms and find that some algorithms, such as trees of Parzen estimators and particle swarm optimization, can select better hyperparameters with only a slight increase in computation time compared to grid search.. Overall, the papers emphasize the importance of tuning the hyperparameters of RVM models to achieve better performance and provide insights into the different methods that can be used for this purpose..

What is the role of tuning parameters in machine learning algorithms?5 answersTuning parameters play a crucial role in machine learning algorithms. It is essential to find the optimal values for these parameters to ensure that the models perform well. Hyperparameter tuning is a technique used to determine the most effective parameters for learning algorithms. Different methods, such as grid search, random search, Bayesian Optimization, Genetic Algorithm, SHERPA, and Optuna, can be used to tune hyperparameters. The objective is to find the best hyperparameter tuning method for each algorithm. Grid search is a commonly used approach to identify ranges of tuning parameters that lead to good models across different datasets. The process involves exploring different optimization algorithms to select a model within the tuning parameter space. The results of hyperparameter tuning can significantly improve the performance of machine learning algorithms, such as Multinomial Logistic Regression, Support Vector Machine, and Artificial Neural Network.

What is the best way to tune hyperparameters?4 answersThe best way to tune hyperparameters is by adopting established best practices from AutoML, such as the separation of tuning and testing seeds, as well as principled hyperparameter optimization (HPO) across a broad search space. This approach has been shown to significantly affect the agent's final performance and sample efficiency in deep reinforcement learning (RL). Comparisons between multiple state-of-the-art HPO tools and hand-tuned counterparts have demonstrated that HPO approaches often have higher performance and lower compute overhead. Additionally, efficient formulas to compute the gradient and hessian of approximate leave-one-out cross-validation have been derived, allowing for the application of second-order optimizers to find hyperparameters. It is important to note that hyperparameter optimization is more nuanced than previously believed, and the specific optimizers that should be endorsed remain to be seen.

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