Ho–Kashyap with Early Stopping vs Soft Margin SVM for Linear Classifiers – An Application
نویسندگان
چکیده
In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early stopping methods. These methods are applied on a non-destructive control application for a 4-class problem of rail defect classification.
منابع مشابه
Ho-Kashyap with Early Stopping Versus Soft Margin SVM for Linear Classifiers - An Application
In a classification problem, hard margin SVMs tend to minimize the generalization error by maximizing the margin. Regularization is obtained with soft margin SVMs which improve performances by relaxing the constraints on the margin maximization. This article shows that comparable performances can be obtained in the linearly separable case with the Ho–Kashyap learning rule associated to early st...
متن کاملHo-Kashyap classifier with early stopping for regularization
This paper focuses on linear classification using a fast and simple algorithm known as the Ho–Kashyap learning rule (HK). In order to avoid overfitting and instead of adding a regularization parameter in the criterion, early stopping is introduced as a regularization method for HK learning, which becomes HKES (Ho–Kashyap with Early Stopping). Furthermore, an automatic procedure, based on genera...
متن کاملSVM Soft Margin Classifiers: Linear Programming versus Quadratic Programming
Support vector machine soft margin classifiers are important learning algorithms for classification problems. They can be stated as convex optimization problems and are suitable for a large data setting. Linear programming SVM classifier is specially efficient for very large size samples. But little is known about its convergence, compared with the well understood quadratic programming SVM clas...
متن کاملSoft Margin Bayes-Point-Machine Classification via Adaptive Direction Sampling
Supervised machine learning is an important building block for many applications that involve data processing and decision making. Good classifiers are trained to produce accurate predictions on a training set while also generalizing well to unseen data. To this end, Bayes-PointMachines (bpm) were proposed in the past as a generalization of margin maximizing classifiers, such as Support-Vector-...
متن کاملContextual Classification of Hyperspectral Images by Support Vector Machines and Markov Random Fields
In the context of hyperspectral-image classification, a key problem is represented by the Hughes’ phenomenon, which makes many supervised classifiers ineffective when applied to high-dimensional feature spaces. Furthermore, most traditional hyperspectral-image classifiers are noncontextual, i.e., they label each pixel based on its spectral signature but while neglecting all interpixel correlati...
متن کامل