In machine learning (ML), a margin classifier is a type of classification model which is able to give an associated distance from the decision boundary...

it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum-margin classifier; or equivalently, the perceptron...

appropriate for certain datasets and goals. A margin classifier is a classification model that utilizes the margin of each example to learn such classification...

learner is defined as a classifier that is only slightly correlated with the true classification. A strong learner is a classifier that is arbitrarily well-correlated...

learning, a linear classifier makes a classification decision for each object based on a linear combination of its features. Such classifiers work well for...

method. The most intuitive nearest neighbour type classifier is the one nearest neighbour classifier that assigns a point x to the class of its closest...

it is known as the maximum-margin hyperplane and the linear classifier it defines is known as a maximum margin classifier. More formally, given some training...

underlying vector space into two sets, one for each class. The classifier will classify all the points on one side of the decision boundary as belonging...

(y=1|x)={\frac {1}{1+\exp(Af(x)+B)}}} , i.e., a logistic transformation of the classifier output f(x), where A and B are two scalar parameters that are learned...

Boosting (LPBoost) is a supervised classifier from the boosting family of classifiers. LPBoost maximizes a margin between training samples of different...

In machine learning, a probabilistic classifier is a classifier that is able to predict, given an observation of an input, a probability distribution over...

vector machine (with a Gaussian kernel) is a nonparametric large-margin classifier. The method of moments with polynomial probability distributions....

regression (LARS) Classifiers Probabilistic classifier Naive Bayes classifier Binary classifier Linear classifier Hierarchical classifier Dimensionality...

Cabestany, Joan (2001). "Oriented principal component analysis for large margin classifiers". Neural Networks. 14 (10): 1447–1461. doi:10.1016/S0893-6080(01)00106-X...

harder-to-classify examples. AdaBoost refers to a particular method of training a boosted classifier. A boosted classifier is a classifier of the form...

perceptron is an algorithm for supervised learning of binary classifiers. A binary classifier is a function that can decide whether or not an input, represented...

for support vector machines (SVMs). For an intended output t = ±1 and a classifier score y, the hinge loss of the prediction y is defined as ℓ ( y ) = max...

comparison to regularized likelihood methods". Advances in Large Margin Classifier: 61–74. Zadrozny, Bianca; Elkan, Charles (2001). "Obtaining Calibrated...

Obermayer, Klaus (2000). "Large Margin Rank Boundaries for Ordinal Regression". Advances in Large Margin Classifiers. MIT Press. pp. 115–132. Rennie,...

A passive margin is the transition between oceanic and continental lithosphere that is not an active plate margin. A passive margin forms by sedimentation...

This classifier is implemented in Weka under the name OneR (for "1-rule"). This is what has been implemented in Weka's DecisionStump classifier. Reyzin...

Metatext NLP Database. Retrieved 26 October 2020. Kim, Byung Joo (2012). "A Classifier for Big Data". Convergence and Hybrid Information Technology. Communications...

classifications}}}} This is usually expressed as a percentage. For example, if a classifier makes ten predictions and nine of them are correct, the accuracy is 90%...

algorithm that generalizes the Support-Vector Machine (SVM) classifier. Whereas the SVM classifier supports binary classification, multiclass classification...

out to be 30 percent." Calibration in classification means transforming classifier scores into class membership probabilities. An overview of calibration...

Guyon, I. M.; Vapnik, V. N. (1992). "A training algorithm for optimal margin classifiers". Proceedings of the fifth annual workshop on Computational learning...

decisions means applying all classifiers to an unseen sample x and predicting the label k for which the corresponding classifier reports the highest confidence...

; Torr P.H.S.; Zisserman A. (2007). "An Invariant Large Margin Nearest Neighbour Classifier". 2007 IEEE 11th International Conference on Computer Vision...

related to the regularization properties of the classifier. Specifically a loss function of larger margin increases regularization and produces better estimates...

September 2000). "Probabilities for SV Machines". Advances in Large-Margin Classifiers: 61–74. doi:10.7551/mitpress/1113.003.0008. ISBN 978-0-262-28397-7...