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563
Top-k Multiclass SVM
"... Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a dire ..."
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Class ambiguity is typical in image classification problems with a large number of classes. When classes are difficult to discriminate, it makes sense to allow k guesses and evaluate classifiers based on the top-k error instead of the standard zero-one loss. We propose top-k multiclass SVM as a
Efficient Approach Multiclass SVM For Vowels Recognition
"... Abstract—In this paper we present and investigate the performance of a simple framework for multiclass problems of support vector machine (SVM), we present a new approach named EAMSVM (Efficient Approach Multiclass SVM), in order to achieve high classification efficiency for multiclass problems. The ..."
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Abstract—In this paper we present and investigate the performance of a simple framework for multiclass problems of support vector machine (SVM), we present a new approach named EAMSVM (Efficient Approach Multiclass SVM), in order to achieve high classification efficiency for multiclass problems
Which is the best multiclass SVM method? An empirical study
- Proceedings of the Sixth International Workshop on Multiple Classifier Systems
, 2005
"... Abstract. Multiclass SVMs are usually implemented by combining several two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method implemented by max-wins voting are popularly used for this purpose. In this paper we give empirical evidence to show that the ..."
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Cited by 74 (0 self)
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Abstract. Multiclass SVMs are usually implemented by combining several two-class SVMs. The one-versus-all method using winner-takes-all strategy and the one-versus-one method implemented by max-wins voting are popularly used for this purpose. In this paper we give empirical evidence to show
A Comparison of Multiclass SVM Methods for Real World Natural Scenes
"... Abstract. Categorization of natural scene images into semantically meaningful categories is a challenging problem that requires usage of multiclass classifica-tion methods. Our objective in this work is to compare multiclass SVM classifi-cation strategies for this task. We compare the approaches whe ..."
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Cited by 1 (0 self)
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Abstract. Categorization of natural scene images into semantically meaningful categories is a challenging problem that requires usage of multiclass classifica-tion methods. Our objective in this work is to compare multiclass SVM classifi-cation strategies for this task. We compare the approaches
Person Independent Facial Expression Detection using MBWM and Multiclass SVM
"... Facial expression analysis is an attractive, challenging and important field of study in facial analysis. It’s important applications include many areas such as human–computer interaction, human emotion analysis, biometric authentication, exhaustion detection and data-driven animation. For successfu ..."
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and Mean based weight matrix for person-independent facial expression recognition. Multiclass SVM is applied systematically for classification. The Japanese female database JAFFE is used for the experiment. Extensive experiments shows that statistical features derived from LBP are effective and efficient
Intelligent Agent-Based Intrusion Detection System Using Enhanced Multiclass SVM
"... Intrusion detection systems were used in the past along with various techniques to detect intrusions in networks effectively. However, most of these systems are able to detect the intruders only with high false alarm rate. In this paper, we propose a new intelligent agent-based intrusion detection ..."
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model for mobile ad hoc networks using a combination of attribute selection, outlier detection, and enhanced multiclass SVM classification methods. For this purpose, an effective preprocessing technique is proposed that improves the detection accuracy and reduces the processing time. Moreover, two new
Is Unlabeled Data Suitable for Multiclass SVM-based Web Page Classification?
"... Support Vector Machines present an interesting and effective approach to solve automated classification tasks. Although it only handles binary and supervised problems by nature, it has been transformed into multiclass and semi-supervised approaches in several works. A previous study on supervised an ..."
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Cited by 2 (2 self)
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and semi-supervised SVM classification over binary taxonomies showed how the latter clearly outperforms the former, proving the suitability of unlabeled data for the learning phase in this kind of tasks. However, the suitability of unlabeled data for multiclass tasks using SVM has never been tested before
Multiclass SVM-Based Isolated-Digit Recognition using a HMM-Guided Segmentation
"... Abstract. Automatic Speech Recognition (ASR) is essentially a problem of pattern classification, however, the time dimension of the speech signal has prevented to pose ASR as a simple static classification problem. Support Vector Machine (SVM) classifiers could provide an appropriate solution, since ..."
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, since they are very well adapted to high-dimensional classification problems. Nevertheless, the use of SVMs for ASR is by no means straightforward, because SVM classifiers are well developed for binary problems but not so for the multiclass case. In this paper we compare two approaches to implement
A Comparison of Methods for Multiclass Support Vector Machines
- IEEE TRANS. NEURAL NETWORKS
, 2002
"... Support vector machines (SVMs) were originally designed for binary classification. How to effectively extend it for multiclass classification is still an ongoing research issue. Several methods have been proposed where typically we construct a multiclass classifier by combining several binary class ..."
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Cited by 952 (22 self)
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classifiers. Some authors also proposed methods that consider all classes at once. As it is computationally more expensive to solve multiclass problems, comparisons of these methods using large-scale problems have not been seriously conducted. Especially for methods solving multiclass SVM in one step, a much
www.elsevier.com/locate/pr Robust and efficient multiclass SVM models for phrase pattern recognition
"... Phrase pattern recognition (phrase chunking) refers to automatic approaches for identifying predefined phrase structures in a stream of text. Support vector machines (SVMs)-based methods had shown excellent performance in many sequential text pattern recognition tasks such as protein name finding, a ..."
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, and noun phrase (NP)-chunking. Even though they yield very accurate results, they are not efficient for online applications, which need to handle hundreds of thousand words in a limited time. In this paper, we firstly re-examine five typical multiclass SVM methods and the adaptation to phrase chunking
Results 1 - 10
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