Book ChapterDOI
Support Vector Machine
Abhisek Ukil
- pp 161-226
About:
The article was published on 2007-01-01. It has received 127 citations till now. The article focuses on the topics: Relevance vector machine & Structured support vector machine.read more
Citations
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Proceedings ArticleDOI
Advantage and drawback of support vector machine functionality
TL;DR: The Support Vector Machine is one of the most efficient machine learning algorithms, which is mostly used for pattern recognition since its introduction in 1990s, and statistics was collected from journals and electronic sources published in the period of 2000 to 2013.
Journal ArticleDOI
A Distance-Based Weighted Undersampling Scheme for Support Vector Machines and its Application to Imbalanced Classification
TL;DR: This work proposes a weighted undersampling (WU) scheme for SVM based on space geometry distance, and thus produces an improved algorithm named WU-SVM, which well outperforms the state-of-the-art methods in terms of three popular metrics for imbalanced classification, i.e., area under the curve, F-Measure, and G-Mean.
SUPPORT VECTOR MACHINE-A Survey
Ashis Pradhan,Svm Model +1 more
TL;DR: A theoretical aspect of SVM is presented, its concepts and its applications overview, which proves that SVM performs better than other network traffic classifier in terms of generalization of problem.
Journal ArticleDOI
Context-Aware Convolutional Neural Network for Object Detection in VHR Remote Sensing Imagery
TL;DR: A context-aware convolutional neural network model for object detection that includes proposal generation, context feature extraction, feature fusion, and classification, and the influence of key factors, such as Context-RoIs, different feature scales, and different spatial context window sizes is thoroughly explored.
Journal ArticleDOI
Automatic diagnosis of fungal keratitis using data augmentation and image fusion with deep convolutional neural network.
TL;DR: The experimental results demonstrate the novel convolutional neural network framework perfectly balances the diagnostic performance and computational complexity, and can improve the effect and real-time performance in the diagnosis of fungal keratitis.
References
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Proceedings ArticleDOI
Advances in kernel methods: support vector learning
TL;DR: Support vector machines for dynamic reconstruction of a chaotic system, Klaus-Robert Muller et al pairwise classification and support vector machines, Ulrich Kressel.