Results 251 to 260 of about 8,989,075 (297)

Support vector machine based ensemble classifier

Proceedings of the 2005, American Control Conference, 2005., 2005
The strategy that the original input space is partitioned into several input subspaces usually works for improving the performance. Different from conventional partition methods, the partition method, attribute reduction based on rough sets theory, allows the input subspaces partially overlapped.
Zhonghui Hu, Yunze Cai, Ye Li, Xiaoming Xu 0001   +3 more
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Reconfigurable Support Vector Machine Classifier with Approximate Computing

2017 IEEE Computer Society Annual Symposium on VLSI (ISVLSI), 2017
Support Vector Machine (SVM) is one of the most popular machine learning algorithms. An energy-efficient SVM classifier is proposed in this paper, where approximate computing is utilized to reduce energy consumption and silicon area. A hardware architecture with reconfigurable kernels and overflow-resilient limiter is presented.
Martin Van Leussen, Jos Huisken, Lei Wang, Hailong Jiao, José Pineda de Gyvez   +4 more
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Classifier cascades for support vector machines

2008 23rd International Conference Image and Vision Computing New Zealand, 2008
Support vector machines (SVMs) are a binary classification technique with a growing popularity in the field of machine learning. While SVMs have shown to deliver good classification performance, in itpsilas original formulation the technique can be computationally complex and therefore slow at run-time.
Ignas Kukenys, Brendan McCane
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A comparison of Extreme Learning Machine and Support Vector Machine classifiers

2015 IEEE International Conference on Intelligent Computer Communication and Processing (ICCP), 2015
The comparison of two classifiers, the Extreme Learning Machine (ELM) and the Support Vector Machine (SVM) is considered for performance, resources used (neurons or support vector kernels) and computational complexity (speed). Both implementations are of similar type (C++ compiled as Octave .mex files) to have a better evaluation of speed and ...
Mihai Bucurica, Radu Dogaru, Ioana Dogaru   +2 more
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Support Vector Machine Classifiers for Asymmetric Proximities

2003
The aim of this paper is to afford classification tasks on asymmetric kernel matrices using Support Vector Machines (SVMs). Ordinary theory for SVMs requires to work with symmetric proximity matrices. In this work we examine the performance of several symmetrization methods in classification tasks.
Alberto Muñoz, Isaac Martín de Diego, Javier M. Moguerza   +2 more
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Support Vector Machine for Large Databases as Classifier

2012
Support Vector Machine (SVM) has been successful in multiple areas and is widely accepted as the best off the shelf algorithm for classification. A standard SVM has O(n3) time and O(n3) space complexities, hence making it limited in its usability for large database.
Rahul Kumar Sevakula, Nishchal K. Verma
openaire   +1 more source

An Evolutionary Support Vector Machines Classifier for Pedestrian Detection

2006 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2006
In a pedestrian detection system, a classifier is usually designed to recognize whether a candidate is a pedestrian. Support vector machines (SVM) has become a primary technique to train a classifier for pedestrian detection. However, it is hard to give the best training model which has a tremendous effect to the performance of a SVM classifier.
D. Chen, Xianbin Cao 0001, Yanwu Xu 0001, Hong Qiao   +3 more
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Fuzzy classifier based on fuzzy support vector machine

Journal of Intelligent & Fuzzy Systems, 2014
Support vector machines (SVMs) have been very successful in pattern recognition and function estimation problems. When SVMs are used for classification, the inputs of the training example are real-valued and the outputs are class label y = ±1. However, in practice, the training examples usually belong to a class with certain fuzzy membership, therefore
Aibing Ji, Songcan Chen, Qiang Hua
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Handling missing values in support vector machine classifiers

Neural Networks, 2005
This paper discusses the task of learning a classifier from observed data containing missing values amongst the inputs which are missing completely at random. A non-parametric perspective is adopted by defining a modified risk taking into account the uncertainty of the predicted outputs when missing values are involved.
Kristiaan Pelckmans, Jos De Brabanter, Johan A. K. Suykens, Bart De Moor   +3 more
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Moderating the outputs of support vector machine classifiers

IEEE Transactions on Neural Networks, 1999
In this paper, we extend the use of moderated outputs to the support vector machine (SVM) by making use of a relationship between SVM and the evidence framework. The moderated output is more in line with the Bayesian idea that the posterior weight distribution should be taken into account upon prediction, and it also alleviates the usual tendency of ...
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