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Support vector machines (SVMs) often contain a large number of support vectors which reduce the run-time speeds of decision functions. In addition, this might cause an overfitting effect where the resulting SVM adapts itself to the noise in the training set rather than the true underlying data distribution and will probably fail to correctly classify unseen examples. To obtain more fast and accurate SVMs, many methods have been proposed to prune SVs in trained SVMs. In this paper, we propose a multi-objective genetic algorithm to reduce the complexity of support vector machines as well as to improve generalization accuracy by the reduction of overfitting. Experiments on four benchmark datasets show that the proposed evolutionary approach can effectively reduce the number of support vectors included in the decision functions of SVMs without sacrificing their classification accuracy.
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For more info visit us at: http://www.siliconmentor.com/ Support vector machines are widely used binary classifiers known for its ability to handle high dimensional data that classifies data by separating classes with a hyper-plane that maximizes the margin between them. The data points that are closest to hyper-plane are known as support vectors. Thus the selected decision boundary will be the one that minimizes the generalization error (by maximizing the margin between classes).
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A support vector machine (SVM) learns the decision surface from two different classes of the input points, there are misclassifications in some of the input points in several applications. In this paper a bi-objective quadratic programming model is utilized and different feature quality measures are optimized simultaneously using the weighting method for solving our bi-objective quadratic programming problem. An important contribution will be added for the proposed bi-objective quadratic programming model by getting different efficient support vectors due to changing the weighting values. The numerical examples, give evidence of the effectiveness of the weighting parameters on reducing the misclassification between two classes of the input points. An interactive procedure will be added to identify the best compromise solution from the generated efficient solutions.
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Capitol Tech Univ Doctoral Presentation -May 2024
CapitolTechU
https://app.box.com/s/z2cfx5b2yooxq1ov1wrd1dezn6af36ux
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
Nguyen Thanh Tu Collection
會考英文
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中 央社
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Dementia (Alzheimer & vasular dementia).
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Envelope of Discrepancy in Orthodontics: Enhancing Precision in Treatment
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An Overview of the Odoo 17 Discuss App.pptx
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MOOD STABLIZERS DRUGS.pptx
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Basic Civil Engineering notes on Transportation Engineering, Modes of Transpo...
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“O BEIJO” EM ARTE .
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Removal Strategy _ FEFO _ Working with Perishable Products in Odoo 17
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Pragya Champions Chalice 2024 Prelims & Finals Q/A set, General Quiz
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Spring gala 2024 photo slideshow - Celebrating School-Community Partnerships
The Last Leaf, a short story by O. Henry
The Last Leaf, a short story by O. Henry
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....................Muslim-Law notes.pdf
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philosophy and it's principles based on the life
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HVAC System | Audit of HVAC System | Audit and regulatory Comploance.pptx
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Exploring Gemini AI and Integration with MuleSoft | MuleSoft Mysore Meetup #45
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UChicago CMSC 23320 - The Best Commit Messages of 2024
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Software testing for project report .pdf
Capitol Tech Univ Doctoral Presentation -May 2024
Capitol Tech Univ Doctoral Presentation -May 2024
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
BỘ LUYỆN NGHE TIẾNG ANH 8 GLOBAL SUCCESS CẢ NĂM (GỒM 12 UNITS, MỖI UNIT GỒM 3...
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文會考英文
Data Selection For Support Vector Machine Classifier
1.
Glenn Fung and
Olvi L. Mangasarian August 2000 20081021 Kuan-Chi-I
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SVM (Linear Separable
Case)
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SVM (Linearly Inseparable
Case)
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MSVM (SLA)
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Comparison
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