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How does Python implement SVM from scratch?

Genetic algorithm

Support vector machine

Protein structure prediction

Artificial neural network

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Book Chapter•DOI Comparison of one-class SVM and two-class SVM for fold recognition Alexander Senf, Xue-wen Chen, Anne Zhang - Show less +2 more 03 Oct 2006 16 Citations	In this paper we propose the use of a one-class SVM to reduce the computational resources required to perform SVM learning and classification.
Journal Article•DOI Pruning Support Vector Machines Without Altering Performances Xun Liang, Rong-Chang Chen, Xinyu Guo - Show less +2 more 01 Oct 2008-IEEE Transactions on Neural Networks 28 Citations	The method also circumvents the problem of explicitly discerning SVs in feature space as the SVM formulation does.
Proceedings Article•DOI Study of optimized SVM for incident prediction of a steel plant in India Sobhan Sarkar, Sammangi Vinay, Vishal Pateshwari, Jhareswar Maiti - Show less +3 more 01 Dec 2016 24 Citations	The results of experiments show that grid search-based SVM outperforms other optimized SVM approaches with 88.0% accuracy.
Proceedings Article•DOI A combined SVM and LDA approach for classification Tao Xiong, Vladimir Cherkassky - Show less +1 more 27 Dec 2005 1K Citations	We show that existing SVM software can be used to solve the SVM/LDA formulation.
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.
Proceedings Article•DOI Classification using support vector machines with graded resolution Lipo Wang, Bing Liu, Chunru Wan - Show less +2 more 25 Jul 2005 33 Citations	Experiments on benchmark data sets show that the generalization performance of the SVM-GR is comparable to the traditional SVM.
Proceedings Article•DOI A comparative analysis of classical and one class SVM classifiers for machine fault detection using vibration signals Rummaan Bin Amir, Sufi Tabassum Gul, Abdul Qayyum Khan - Show less +2 more 01 Oct 2016 17 Citations	Classification results obtained using one class SVM are superior than classical SVM as advocated by our simulations.
Journal Article•DOI SVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming Qiang Wu, Ding-Xuan Zhou - Show less +1 more 01 May 2005-Neural Computation 179 Citations	Our analysis shows that the convergence behavior of the linear programming SVM is almost the same as that of the quadratic programming SVM.

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