multiclass classification
X-Attributes Classifier (XAC): A New Multiclass Classification Method by Using Simple Linear Regression and Its Geometrical Properties
... Abstract—In this paper, a new multiclass classification method has been proposed. During the training process, the simple linear regression was used to find the linear relationship between the paired ...
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Grammatical Error Correction as Multiclass Classification with Single Model
... sity (SJT1). Grammatical error detection and correc- tion problem is treated as multiclass classification task. Unlike previous works (Dahlmeier et al., 2012; Ro- zovskaya et al., 2012; Kochmar et al., ...
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Confidence Sets with Expected Sizes for Multiclass Classification
... the multiclass classification framework, the goal of this algorithm is to build the smallest set of labels such that its classification error is below a pre-specified ...the classification ...
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Multiclass Classification of Brain Tumor in MR Images
... MRI is the standard technique to diagnose brain tumor which is widely used by doctors for manual segmentation. Computer aided analysis reduces the workload of doctor. Normally doctors do the biopsy for defining the ...
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On the Consistency of Multiclass Classification Methods
... the classification method based on Ψ is Bayes ...of classification calibration for convex loss ...at classification calibration for binary classification in order to motivate our geometric ...
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Multiclass Classification with Multi-Prototype Support Vector Machines
... The relevance vector machine (RVM) in (Tipping, 2001) is a model used for regression and classification exploiting a probabilistic Bayesian learning framework. It introduces a prior over the weights of the model ...
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Multiclass Classification of XSS Web Page Attack using Machine Learning Techniques
... makes classification decisions by calculating the probabilities and costs related to each ...This classification method usually achieves high recognition rate keeping a low computational ...
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Iterative ensemble feature selection for multiclass classification of imbalanced microarray data
... Figure 1 reports the classification accuracies of IEFS framework and other compared feature selection meth- ods (Filter Ranking, FCBF and mRMR) using both KNN and SVM. The results on GCM data show that the clas- ...
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A LeNet Based Convolution Neural Network for Image Steganalysis on Multiclass Classification
... Prediction Model. The training image set uses the original images, the DCT stego images, the LSB stego images and the histogram stego images, and uses the binary classification method to train three models. The ...
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Electron-Impact Ionization of Boronfluorides BFx (x=1, 2 & 3)
... solve classification problem with remote sensing data in term of classification accuracy and computational ...(ECOC)based multiclass approaches creates many binary classifiers and combines their ...
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An Easy-to-hard Learning Paradigm for Multiple Classes and Multiple Labels
... Many applications can be formulated as a multiclass classification problem. For example, human action recognition aims to classify videos into different categories of human action. In the KTH data set (Sch¨ ...
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PREDICTIVE DATA MINING FRAMEWORK FOR MEDICAL DATA
... for multiclass classification from large of collection predictive data, and present a framework which will work on the cross platform to show data classified ...features, classification techniques ...
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Recognition of Mental Workload Levels using Diagnosis of ECG Signals
... hierarchical classification method showed better classification performance than the conventional multiclass classification method with despite the loss in accuracy and sensitivities certain ...
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Depression Among Pregnant Latinas In South Carolina
... following multiclass classification procedures: support vector machines with linear (SVM-L) and radial (SVM-R) kernels in Cortes and Vapnik (1995); Vapnik (2013), random forest (RF) in Breiman (2001), ...
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Categorization of Non-Melanoma Skin Lesion Diseases Using Support Vector Machine and Its Variants
... as multiclass classification task and offers solution using supervised learning ...using multiclass support vector machine and its variants such as proximal support vector machine and active support ...
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Multiclass EEG motor imagery classification with sub band common spatial patterns
... EEG classification [10– 14] while for better and more control commands of real- time applications such as BCI controlled wheelchair, neuro-gaming, and prosthetics, binary class is not suffi- cient; therefore, a ...
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Step Size Adaptation in Reproducing Kernel Hilbert Space
... This paper presents an online support vector machine (SVM) that uses the stochastic meta-descent (SMD) algorithm to adapt its step size automatically. We formulate the online learning problem as a stochastic gradient ...
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Power transformer dissolved gas analysis through Bayesian networks and hypothesis testing
... (multiclass classification, IEC TC 10 unbalanced dataset, 80% training and 20% testing) including ratio-based DGA, discrete Bayesian network models, and continuous Bayesian network models which do not take ...
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Predicting Human Assessment of Machine Translation Quality by Combining Automatic Evaluation Metrics using Binary Classifiers
... standard classification learning algorithms in order to predict the subjective grades assigned by humans for a given ...such multiclass categorization problems by (1) dividing the multiclass problem ...
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Multiclass Brain Tumor Classification using SVM
... in multiclass classification of brain ...for classification. In the last stage classification method, Support Vector Machine (SVM) for multi class data is ...
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