A novel and quick SVM-based multi-class classifier

Liu, Yiguang; Zhou, You; Cao, Liping

doi:10.1016/j.patcog.2006.05.034

Cited by 61 publications

(28 citation statements)

References 15 publications

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“…Max-Win [13] algorithm is probably the most typical 1-v-1 method for multiclass recognition. The total number of classifiers is N (N − 1)/2 for an N-class problem [14].…”

Section: Mapped Vectorsmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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Recommended by Subhas MukhopadhyayFish species identification and automated fish freshness assessment play important roles in fishery industry applications. This paper describes a method based on support vector machines (SVMs) to improve the performance of fish identification systems. The result is used for the assessment of fish freshness using artificial neural network (ANN). Identification of the fish species involves processing of the images of fish. The most efficient features were extracted and combined with the down-sampled version of the images to create a 1D input vector. Max-Win algorithm applied to the SVM-based classifiers has enhanced the reliability of sorting to 96.46%. The realisation of Cyranose 320 Electronic nose (E-nose), in order to evaluate the fish freshness in real-time, is experimented. Intelligent processing of the sensor patterns involves the use of a dedicated ANN for each species under study. The best estimation of freshness was provided by the most sensitive sensors. Data was collected from four selected species of fishes over a period of ten days. It was concluded that the performance can be increased using individual trained ANN for each specie. The proposed system has been successful in identifying the number of days after catching the fish with an accuracy of up to 91%.

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“…Max-Win [13] algorithm is probably the most typical 1-v-1 method for multiclass recognition. The total number of classifiers is N (N − 1)/2 for an N-class problem [14].…”

Section: Mapped Vectorsmentioning

confidence: 99%

Intelligent Fish Freshness Assessment

Hosseini

Luo

et al. 2008

Journal of Sensors

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“…By coupling these diversities, we investigate several classification methods. The following table summarized these differences, in terms of the optimized criterion, the parameters to be estimated, β i 's or α i 's, as well as the sparsity of the solution: 5 In principle, ℓ functions can encode up to 2 ℓ different classes. However, the design of an optimal coding matrix for such task for the maximum number of classes requires prior information on the samples, often unavailable in practice.…”

Section: The Proposed Frameworkmentioning

confidence: 99%

“…These classification techniques have been thoroughly investigated, and their performance well studied and understood. One seeks to generalize these approaches for multiclass tasks [4,5], which find numerous applications in signal processing [6,7,8,9].…”

Section: Introductionmentioning

confidence: 99%

Multiclass classification machines with the complexity of a single binary classifier

2013

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In this paper, we study the multiclass classification problem. We derive a framework to solve this problem by providing algorithms with the complexity of a single binary classifier. The resulting multiclass machines can be decomposed into two categories. The first category corresponds to vector-output machines, where we develop several algorithms. In the second category, we show that the least-squares classifier can be easily cast into a multiclass one-versus-all scheme, without the need to train multiple binary classifiers. The proposed framework shows that, while keeping the classification accuracy essentially unchanged, the computational complexity is orders of magnitude lower than those previously reported in the literature. This makes our approach extremely powerful and conceptually simple. Moreover, we study the coding of the multiclass labels, and demonstrate that several celebrated approaches are equivalent. These arguments are illustrated with experimentations on well-known benchmarks.

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“…To demonstrate the performance of the clustering procedure in Table 4.1, the IRIS data set (which has been used in our previous research [29]) is employed, together with Haberman and WPBC (available at http:// archive.ics.uci.edu/ml/). IRIS data has three classes: Setosa, Versicolour as well as Virginica, and each of them has 50 instances.…”

Section: Examplementioning

confidence: 99%

Dynamics of a mean-shift-like algorithm and its applications on clustering

Liu¹,

Li²,

Wu³

et al. 2013

Information Processing Letters

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A novel and quick SVM-based multi-class classifier

Cited by 61 publications

References 15 publications

Intelligent Fish Freshness Assessment

Intelligent Fish Freshness Assessment

Multiclass classification machines with the complexity of a single binary classifier

Dynamics of a mean-shift-like algorithm and its applications on clustering

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