Rmsvm.com

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Reinforced Multicategory Support Vector Machines

• A computational algorithm of the RMSVM is given in Section 3
• In Sec-tion 4, we use both simulated examples and an application to lung cancer Microarray data to illustrate performance of the proposed RMSVMs with different choices of the combin-ing weight parameter
• Some discussion and remarks are given in Section 5, followed by

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A New Algorithm For Computation Of A Regularization

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Ultrasonographic Feature Selection And Pattern

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• A rough margin based support vector machine (RMSVM) classifier was proposed to improve the accuracy of ultrasound diagnoses for cervical lymph nodes
• Thirty-six features belonging to 10 kinds of ultrasonographic characteristics were

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A Rough Margin Based Support Vector Machine Information

By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to …

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Ultrasonographic Feature Selection And Pattern

• A rough margin based support vector machine (RMSVM) classifier was proposed to improve the accuracy of ultrasound diagnoses for cervical lymph nodes
• Thirty-six features belonging to 10 kinds of ultrasonographic characteristics were extracted for each of 110 lymph nodes in ultrasonograms.

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(PDF) Rough Margin-Based Linear V Support Vector Machine.

• Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin
• In this way, more data points are adaptively

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A Rough Margin Based Support Vector Machine

• By introducing the rough set theory into the support vector machine (SVM), a rough margin based SVM (RMSVM) is proposed to deal with the overfitting problem due to outliers
• Similar to the classical SVM, the RMSVM searches for the separating hyper-plane that maximizes the rough margin, defined by the lower and upper margin.

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HandProj/Tracker.cpp At Master · Gengshan-y/handProj · GitHub

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• - handProj/Tracker.cpp at master · gengshan-y/handProj

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CiteSeerX — Rough Margin Based Core Vector Machine

• CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract
• 1 The recently proposed rough margin based support vector machine (RMSVM) could tackle the overfitting problem due to outliers effectively with the help of rough margins
• However, the standard solvers for them are time consuming and not feasible for large datasets.

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A New Algorithm For Computation Of A Regularization

• The standard L2-norm support vector machine (SVM) is a widely used tool for classification problems
• The L1-norm SVM is a variant of the standard L2-norm SVM, that constrains the L1-norm of the

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Rough Margin Based Core Vector Machine

• 1 The recently proposed rough margin based support vector machine (RMSVM) could tackle the overfitting problem due to outliers effectively with the help of rough margins
• However, the standard solvers for them are time consuming and not feasible for large datasets
• On the other hand, the core vector machine (CVM) is an optimization

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CiteSeerX — Robust Truncated-hinge-loss Support Vector

• CiteSeerX - Document Details (Isaac Councill, Lee Giles, Pradeep Teregowda): Abstract
• With its elegant margin theory and accurate classification perfor-mance, the Support Vector Machine (SVM) has been widely applied in both machine learning and statistics
• Despite its success and popularity, it still has some drawbacks in certain situations.

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