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A hierarchical classifier using new support vector machine
Eighth International Conference on Document Analysis and Recognition (ICDAR'05), 2005A binary hierarchical classifier is proposed to solve the multi-class classification problem. We also require rejection of non-target inputs, which produces a very difficult problem. The SVRDM (support vector representation and discrimination machine) classifier is considered at each node in the hierarchy, since it offers good generalization and ...
Yu-Chiang Wang 0001, David P. Casasent
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Support vector machine classifiers for sequential decision problems
Proceedings of the 48h IEEE Conference on Decision and Control (CDC) held jointly with 2009 28th Chinese Control Conference, 2009Classification problems in critical applications such as health care or security often require very high reliability because of the high costs of errors. In order to achieve this reliability, such systems often require the use of sequential inspections, where additional data can be collected to resolve ambiguous test cases.
Eladio Rodriguez-Diaz, David A. Castaon
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Patients on weaning trials classified with support vector machines
Physiological Measurement, 2010The process of discontinuing mechanical ventilation is called weaning and is one of the most challenging problems in intensive care. An unnecessary delay in the discontinuation process and an early weaning trial are undesirable. This study aims to characterize the respiratory pattern through features that permit the identification of patients ...
Garde, A +5 more
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A Sparse Support Vector Machine Classifier with Nonparametric Discriminants
2012This paper introduces a novel Sparse Support Vector Machine model with Kernel Nonparametric Discriminants (SSVMKND) which combines data distribution information from two classifiers, namely, the Kernel Support Vector Machine (KSVM) and the Kernel Nonparametric Discriminant (KND).
Naimul Mefraz Khan +3 more
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Fuzzy Theory Based Support Vector Machine Classifier
2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery, 2008Support vector machine (SVM) has become a popular tool in the area of pattern recognition, combining support vector machines with other theories has been proposed as a new direction to improve classification performance. This paper applies fuzzy theory to support vector machines for classification.
Xuehua Li, Lan Shu
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RadSS: A radiolarian classifier using support vector machines
2016 7th International Conference on Information, Intelligence, Systems & Applications (IISA), 2016Radiolarian assemblages have played a significant role as a biostratigraphic and paleoenvironmental tool used in the geological settings. These species can be used in studying sediments lacking calcareous fossils. Easy identification of these species would allow micropaleontologists to proceed further into studying the structure and way of living of ...
Louise Ann Apostol +3 more
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Combining Support Vector Machines for Classifying Fingerprint Images
2020We propose to combine support vector machine (SVM) models learned from different visual features for efficiently classifying fingerprint images. Real datasets of fingerprint images are collected from students at the Can Tho University. The SVM algorithm learns classification models from the handcrafted features such as the scale-invariant feature ...
The-Phi Pham +3 more
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Classifying Galaxy Images through Support Vector Machines
2015 IEEE International Conference on Information Reuse and Integration, 2015Galaxies in the universe are commonly classified by their morphology, or visual appearance. The morphology of a galaxy tells us about the history and physical make-up of the galaxy. With the fast pace at which digital galaxy images are captured and a slow and biased human pattern recognition process, finding an efficient way to automate the galaxy ...
Kathy Applebaum, Du Zhang
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Incremental Learning of Support Vector Machines by Classifier Combining
2007How to acquire new knowledge from new added training data while retaining the knowledge learned before is an important problem for incremental learning. In order to handle this problem, we propose a novel algorithm that enables support vector machines to accommodate new data, including samples that correspond to previously unseen classes, while it ...
Yimin Wen, Bao-Liang Lu
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Classifying ear disorders using support vector machines
2010 Second International Conference on Computational Intelligence and Natural Computing, 2010One of the most significant causes of iatrogenic injury, death and costs in hospitals is medication errors. A medical decision-support system can help physicians to improve the safety, quality and efficiency of healthcare. In this paper we focus on development of a decision-support system for diagnosis of ear disorders.
MOEIN, Mahsa +3 more
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