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عنوان فارسی مقاله:

دسته های خوشه بندی در ماشین های بردار پشتیبانی

عنوان انگلیسی مقاله:

Clustering categories in support vector machines

سال انتشار : 2017

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3. Strategies for the CLSVM 

In this section four different strategies are proposed to obtain the CLSVM classifier. The first, and natural, way to define a CLSVM classifier is by clustering the categories using the scores of the SVM in the original feature space, the SVMO. This is a cheap strategy but underperforming in some cases in terms of accuracy, as we will see in the computational section. Three alternative strategies are proposed based on the two mathematical optimization formulations introduced in Section 2, the CL and the CL-bigM. In the remainder of this section, when describing the strategies, we will explain how to obtain the partial solution ðω;ω0 ; bÞ, which determines the CLSVM classifier, and the assignment vector zn , defining the clustering for the categorical features and thus the clustered feature space, as shown in Fig. 1. The first strategy, the centroid SVM (SVMC ) Strategy, is based on the SVMO scores. The strategy is as follows. The SVMO classifier is built, the categories of feature j are clustered into Lj clusters finding a partition of the SVMO scores, for each j, and the SVM classifier built in the clustered feature space is returned as the CLSVM classifier. The pseudocode of this strategy can be found in Fig. 2. There, the partition of the SVMO scores is found by solving the minimum sum of squares clustering (MSSC) problem, [19], which is polynomially solvable for one-dimensional data when the number of clusters is fixed [1,20,30]. Given a categorical feature j, the MSSC problem clusters all the categories into Lj clusters such that the sum of the squared distance of the score of a category from the centroid of the cluster is minimized. The SVMC Strategy can be implemented using other partitions of the SVMO scores instead of the one given by MSSC. For instance, one can use natural values to partition the scores, such as 0, placing the negative scores in the first cluster, the zero ones in the second cluster, and the remaining ones in the third cluster. Other natural values are the median score, yielding a partition into two clusters, or, more generally, percentiles of the scores. The second strategy, the CL randomized rounding (CLRR) Strategy, performs a randomized rounding [27], to the fractional assignment vector returned by the continuous relaxation of the CL formulation. This is a QCQP formulation, where constraint (11) is relaxed to zA½0; 1 PJ j ¼ 1 LjKj . The pseudocode of this reduction strategy can be found in Fig. 3, where rand(p) is a subroutine of random numbers generation, returning the value 1 with probability p and 0 otherwise.

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Support vector machine - Wikipedia https://en.wikipedia.org/wiki/Support_vector_machine In machine learning, support vector machines (SVMs, also support vector networks) are supervised learning models with associated learning algorithms that analyze data used for classification and regression analysis. ‎Kernel method · ‎Vladimir Vapnik · ‎Hyperplane · ‎Linear classifier 1.4. Support Vector Machines — scikit-learn 0.18.2 documentation scikit-learn.org/stable/modules/svm.html The support vector machines in scikit-learn support both dense ( numpy.ndarray and convertible to that by numpy.asarray ) and sparse (any scipy.sparse ) ... Understanding Support Vector Machine algorithm from examples ... https://www.analyticsvidhya.com › Business Analytics Oct 6, 2015 - “Support Vector Machine” (SVM) is a supervised machine learning algorithm which can be used for both classification or regression challenges. Introduction to Support Vector Machines — OpenCV 2.4.13.2 ... docs.opencv.org › OpenCV Tutorials › ml module. Machine Learning Then, the operation of the SVM algorithm is based on finding the hyperplane that gives the largest minimum distance to the training examples. Twice, this ... [PDF]Support Vector Machine Tutorial - Columbia CS www.cs.columbia.edu/~kathy/cs4701/documents/jason_svm_tutorial.pdf by J Weston - ‎Cited by 10 - ‎Related articles Support Vector Machine. (and Statistical Learning Theory). Tutorial. Jason Weston. NEC Labs America. 4 Independence Way, Princeton, USA. Support Vector Machines for Machine Learning - Machine Learning ... machinelearningmastery.com/support-vector-machines-for-machine-learning/ Apr 20, 2016 - Support Vector Machines are perhaps one of the most popular and talked about machine learning algorithms. They were extremely popular ... [PDF]A Tutorial on Support Vector Machines for Pattern Recognition - CMAP www.cmap.polytechnique.fr/~mallat/papiers/svmtutorial.pdf by CJC BURGES - ‎Cited by 17830 - ‎Related articles We then describe linear Support Vector Machines (SVMs) for separable and non-separable ... We show how Support Vector machines can have very large.