Combining MLC and SVM classifiers for learning based decision making: analysis and evaluations
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Cites methods from "Combining MLC and SVM classifiers f..."
...The machine learning technique is somewhat desired [10] since the kernel function is powerful in classification problems [11]....
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Cites background or methods or result from "Combining MLC and SVM classifiers f..."
...Due to a relatively competitive strength of the MLC and SVM methods, as shown in a number of previous studies, the performance of these two supervised classifiers appears to be different from one case study to another (Zhang, Ren, and Jiang 2015)....
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...Differently from MLC, which is a parametric method based on Bayesian approach, SVM is an optimization-based non-parametric machine learning method (Zhang, Ren, and Jiang 2015)....
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...The findings in this study are in accordance with several other studies (Pal and Mather 2005; Zhang, Ren, and Jiang 2015); the SVM method outperforms MLC for all three sensors regardless of the spectral range or spectral and spatial resolutions of the data....
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...…SVM classifiers have different underlying statistics, which is important to consider when exploring the performance of a new sensor such as HSI2 (Zhang, Ren, and Jiang 2015); 2) The two classifiers are commonly competing in their performance although SVM performed better for hyperspectral data…...
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Cites background from "Combining MLC and SVM classifiers f..."
...As reported in the literature, many vector-based multimedia analysis and classification methods have been presented, of which representative techniques include K-nearest neighbor (KNN) [9][10], support vector regression (SVR) [11][12], and logistic regression (LR) [13] etc....
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References
40,826 citations
"Combining MLC and SVM classifiers f..." refers methods in this paper
...Among these four datasets, SamplesNew is a dataset of suspicious micro-classification clusters extracted from [16] and svmguide3 is a demo dataset of practical svm guide [28], whilst sonar and splice datasets come from the UCI repository of machine learning databases [29]....
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...Stage 1: SVM for initial training and classification The open source library libSVM [28] is used for initial training and classification of the aforementioned four datasets, and both the linear and the Gaussian radial basis (RBF) kernels are tested....
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37,861 citations
"Combining MLC and SVM classifiers f..." refers background in this paper
...In Cortes and Vapnik [21], the principles of SVM are comprehensively discussed....
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...In Cortes and Vapnik [22], the principles of SVM are comprehensively discussed....
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...Machine Learning, 2011 [22] Cortes, C., Vapnik, V., Support-vector networks, Machine Learning, 20: 273-297, 1995 [23] Hsu, C.-W., Lin, C.-J., A Comparison of Methods for Multiclass Support Vector Machines, IEEE Transactions on Neural Networks, 13(2): 415-425, 2002 [24] Lee, Y., Lin, Y., Wahba, G., Multicategory Support Vector Machines, Theory, and Application to the Classification of Microarray Data and Satellite Radiance Data, J. Amer....
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6,562 citations
"Combining MLC and SVM classifiers f..." refers background or methods in this paper
...2 Results from a RBF-kernelled SVM and the MLC In this group of experiments, the RBF kernel is used for the SVM in the combined classifier as it is popularly used in various classification problems [16, 23]....
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...Some useful further readings can be found in [23], [24] and [25]....
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4,584 citations
"Combining MLC and SVM classifiers f..." refers methods in this paper
...In Platt [25], a posterior class probability p i is estimated by a sigmoid function as follows:...
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...In Platt [26], a posterior class probability ip is estimated by a sigmoid function below....
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...[25] Crammer, K., Singer, Y., On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines, Journal of Machine Learning Research 2: 265–292, 2001 [26] Platt, J., Probabilistic Outputs for Support Vector Machines and Comparisons to Regularized Likelihood Methods, In: A. Smola, P. Bartlett, B. Scholkopf, and D. Schuurmans (eds.)...
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...In addition, in Lin et al [27] Platt’s approach is further improved to avoid any numerical difficulty, i.e. overflow or underflow, in determining ip in cases BAgE iSVMi )(x is either too large or too small. otherwiseee Eife p ii i EE i E i 1 1 )1( 0)1( (24) Although there are significant differences between SVM and MLC, the probabilistic model above has uncovered the connection between these two classifiers....
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...Cambridge, MA., 2000 [27] Lin, H.-T., Lin, C. J., Weng, R. C., A note on Platt’s probabilistic outputs for support vector machines, Journal of Machine Learning, 68(3): 267-276, 2007....
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2,214 citations
"Combining MLC and SVM classifiers f..." refers background in this paper
...Some useful further readings can be found in [23], [24] and [25]....
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