Loading...
Please wait, while we are loading the content...
Similar Documents
Reducing multiclass to binary: A unifying approach for margin classifiers (2000)
| Content Provider | CiteSeerX |
|---|---|
| Author | Allwein, Erin L. Schapire, Robert E. Singer, Yoram |
| Abstract | We present a unifying framework for studying the solution of multiclass categorization problems by reducing them to multiple binary problems that are then solved using a margin-based binary learning algorithm. The proposed framework unifies some of the most popular approaches in which each class is compared against all others, or in which all pairs of classes are compared to each other, or in which output codes with error-correcting properties are used. We propose a general method for combining the classifiers generated on the binary problems, and we prove a general empirical multiclass loss bound given the empirical loss of the individual binary learning algorithms. The scheme and the corresponding bounds apply to many popular classification learning algorithms including support-vector machines, AdaBoost, regression, logistic regression and decision-tree algorithms. We also give a multiclass generalization error analysis for general output codes with AdaBoost as the binary learner. Experimental results with SVM and AdaBoost show that our scheme provides a viable alternative to the most commonly used multiclass algorithms. |
| File Format | |
| Publisher Date | 2000-01-01 |
| Access Restriction | Open |
| Subject Keyword | Binary Problem Logistic Regression Support-vector Machine Error-correcting Property Binary Learner Corresponding Bound Used Multiclass Algorithm Decision-tree Algorithm Empirical Loss Viable Alternative Multiclass Generalization Error Analysis General Method Individual Binary Learning Algorithm Margin Classifier Popular Approach Unifying Approach Output Code Multiclass Categorization Problem Margin-based Binary Learning Algorithm Experimental Result Unifying Framework General Empirical Multiclass Loss Bound Many Popular Classification General Output Code |
| Content Type | Text |
