multiclass vector support machines
Multiclass Classification Using Support Vector Machines
... for multiclass classification and introduce the Divide and Conquer Support Vector Machine (DCSVM) algorithm which relies on data sparsity in high dimensional space and performs a smart partitioning ...
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Comparison of Image Classification Techniques: Binary and Multiclass using Convolutional Neural Network and Support Vector Machines
... This research study focuses on accuracy measure of the above mentioned methods. For the image classification studied in this paper, it has been observed that SVM gives adequate accuracy for binary classification whereas ...
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On Nearest-Neighbor Error-Correcting Output Codes with Application to All-Pairs Multiclass Support Vector Machines
... We evaluated the performance of different ECOCs using the eleven standard datasets listed in Ta- ble 1. The datasets soybean-large, vowel, isolet, letter, satimage and pendigits are available at the UCI repository, with ...
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Two Criteria for Model Selection in Multiclass Support Vector Machines
... for multiclass SVMs, we are often required to solve larger scale optimization ...the multiclass setting to tune model ...a multiclass SVM using the ECOC approach is developed and applied to the model ...
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On the Algorithmic Implementation of Multiclass Kernel-based Vector Machines
... a multiclass kernel-machine on the MNIST dataset using a Pentium III computer running at 600MHz with 2Gb of physical ...to multiclass problems will become even more evident in problems with a large number ...
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Laplacian Support Vector Machines Trained in the Primal
... We selected eight popular data sets for our experiments. Most of them data sets has been already used in previous works to evaluate several semi-supervised classification algorithms (Sindhwani et al., 2005; Belkin et ...
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Numerical Experiments with Support Vector Machines
... The present study has dealt with the simplest problem of two-class classification with the Support Vector Machines. By performing the present study we have postponed jump to more interesting and ...
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Creating a quality map of a slate deposit using support vector machines
... of support vector machines (SVMs)—SVM classification (multiclass one-against-all), ordinal SVM and SVM regression—are used to draw up the quality ...
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Multiclass Support Vector Machine with New Kernel for EEG Classification
... the Multiclass Support Vector Machines with new Kernel (MSVM) for EEG (Electroencephalogram) signals classification problem with hybrid domain ...the Multiclass SVM classification ...
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Multiclass Latent Locally Linear Support Vector Machines
... and, as before, for ML3 and OCC we concatenated 1 to each instance vector. Please note that the dimensionality of Banana, Liver, PIMA and WDBC is lower than 10, resulting in a reduced number of models (2, 6, 8 and ...
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Multiclass Classification with Multi-Prototype Support Vector Machines
... relevance vector machine (RVM) in (Tipping, 2001) is a model used for regression and classification exploiting a probabilistic Bayesian learning ...of support vectors after the classifiers have been ...
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Multiclass Classification with Cross Entropy-Support Vector Machines
... solving multiclass support vector machine ...of support vectors ...tackle multiclass classification problem: one-against-rest (OAR) and one-against- one ...solve multiclass ...
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Multiclass latent locally linear support vector machines
... weighted combination of linear models (as in Yu et al. (2009) and Ladicky and Torr (2011)). Our idea is to locally find a good combination of models that maximizes the confidence of the full model on each sample. The ...
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Scalable Multilevel Support Vector Machines
... optimize this solution for the current fine level i . Unlike many other multilevel algorithms, in which the inherited coarse solution contains projected variables only, we initially inherit not only the coarse ...
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Covering Numbers and Support Vector Machines
... The main technical result of this paper is a covering number bound based on this result that is amenable to direct calculation. We illustrate the new result by bounding the covering numbers of SV machines which ...
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Rating Companies with Support Vector Machines
... direction vector of the separating hyperplane, it can be estimated differently by the SVM and DA without affecting much the accuracy since the correlation of underlying predictors is ...
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Support vector machines for texture classification
... Abstract—This paper investigates the application of support vector machines (SVMs) in texture classification. Instead of relying on an external feature extractor, the SVM receives the gray-level ...
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Support Vector Machines for Face Recognition
... etc. Support vector machine (SVM) learning is a recent technology that gives a decent broad view performance this paper given the most recent algorithms developed for face recognition and tries to give an ...
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Robustness and Regularization of Support Vector Machines
... We consider regularized support vector machines (SVMs) and show that they are precisely equiva- lent to a new robust optimization formulation. We show that this equivalence of robust optimization and ...
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On qualitative robustness of support vector machines
... Support vector machines (SVMs) have attracted much attention in theoretical and in applied statistics. The main topics of recent interest are consistency, learning rates and robustness. We address ...
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