Problem in machine learning and statistical classification
Not to be confused with multi-label classification.
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In machine learning and statistical classification, multiclass classification or multinomial classification is the problem of classifying instances into one of three or more classes (classifying instances into one of two classes is called binary classification).
While many classification algorithms (notably multinomial logistic regression) naturally permit the use of more than two classes, some are by nature binary algorithms; these can, however, be turned into multinomial classifiers by a variety of strategies.
Multiclass classification should not be confused with multi-label classification, where multiple labels are to be predicted for each instance.
and 26 Related for: Multiclass classification information
In machine learning and statistical classification, multiclassclassification or multinomial classification is the problem of classifying instances into...
observation. Classification can be thought of as two separate problems – binary classification and multiclassclassification. In binary classification, a better...
in multiclassclassification, accuracy is simply the fraction of correct classifications: Accuracy = correct classifications all classifications {\displaystyle...
Multiclass may refer to: Multiclassclassification, in machine learning Having multiple character classes in a role-playing game Character class (Dungeons...
casts the multiclassclassification problem into a single optimization problem, rather than decomposing it into multiple binary classification problems...
classification and multiclassclassification. In binary classification, a better understood task, only two classes are involved, whereas multiclass classification...
multiclassclassification networks. These activations perform aggregation over the inputs, such as taking the mean, minimum or maximum. In multiclass...
by logistic regression classifiers. Proof Consider a generic multiclassclassification problem, with possible classes Y ∈ { 1 , . . . , n } {\displaystyle...
to Platt's method when sufficient training data is available. In the multiclass case, one can use a reduction to binary tasks, followed by univariate...
various multiclassclassification methods, such as multinomial logistic regression (also known as softmax regression),: 206–209 multiclass linear discriminant...
training linear classifiers, the perceptron generalizes naturally to multiclassclassification. Here, the input x {\displaystyle x} and the output y {\displaystyle...
by enough margin). While binary SVMs are commonly extended to multiclassclassification in a one-vs.-all or one-vs.-one fashion, it is also possible to...
S2CID 233550030. Prinzie, A.; Van den Poel, D. (2008). "Random Forests for multiclassclassification: Random MultiNomial Logit". Expert Systems with Applications....
multinomial logistic regression is a classification method that generalizes logistic regression to multiclass problems, i.e. with more than two possible...
appealing because they naturally handle both regression and (multiclass) classification, are relatively fast to train and to predict, depend only on one...
classifier. Whereas the SVM classifier supports binary classification, multiclassclassification and regression, the structured SVM allows training of...
variable Gaussian process model with Pitman–Yor process priors for multiclassclassification". Neurocomputing. 120: 482–489. doi:10.1016/j.neucom.2013.04.029...
Rui Zhang (2012). "Extreme Learning Machine for Regression and MulticlassClassification" (PDF). IEEE Transactions on Systems, Man, and Cybernetics - Part...
an alternative to the multinomial logit model as one method of multiclassclassification. It is not to be confused with the multivariate probit model,...
convolutional neural network (CNN), have also been employed. Binary or multiclassclassification methods for functional annotation generally produce less accurate...
Variable Gaussian Process Model with Pitman-Yor Process Priors for MulticlassClassification," Neurocomputing, vol. 120, pp. 482–489, Nov. 2013. doi:10.1016/j...
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