On the Consistency of Multiclass Classification Methods
نویسندگان
چکیده
On the Consistency of Multiclass Classification Methods
منابع مشابه
On the Consistency of Output Code Based Learning Algorithms for Multiclass Learning Problems
A popular approach to solving multiclass learning problems is to reduce them to a set of binary classification problems through some output code matrix: the widely used one-vs-all and all-pairs methods, and the error-correcting output code methods of Dietterich and Bakiri (1995), can all be viewed as special cases of this approach. In this paper, we consider the question of statistical consiste...
متن کاملConsistency of Multiclass Empirical Risk Minimization Methods Based on Convex Loss
The consistency of classification algorithm plays a central role in statistical learning theory. A consistent algorithm guarantees us that taking more samples essentially suffices to roughly reconstruct the unknown distribution. We consider the consistency of ERM scheme over classes of combinations of very simple rules (base classifiers) in multiclass classification. Our approach is, under some...
متن کاملConsistency versus Realizable H-Consistency for Multiclass Classification
A consistent loss function for multiclass classification is one such that for any source of labeled examples, any tuple of scoring functions that minimizes the expected loss will have classification accuracy close to that of the Bayes optimal classifier. While consistency has been proposed as a desirable property for multiclass loss functions, we give experimental and theoretical results exhibi...
متن کاملAdversarial Multiclass Classification: A Risk Minimization Perspective
Recently proposed adversarial classification methods have shown promising results for cost sensitive and multivariate losses. In contrast with empirical risk minimization (ERM) methods, which use convex surrogate losses to approximate the desired non-convex target loss function, adversarial methods minimize non-convex losses by treating the properties of the training data as being uncertain and...
متن کاملConsistency of surrogate risk minimization methods for multiclass 0 - 1 classification
Binary classification Multiclass classification Label space, Y and {±1} [n] Prediction space, T Target 0-1 loss `0-1 : {±1} × {±1} 7→ R+ `0-1 : [n]× [n] 7→ R+ `0-1 : Y × T 7→ R+ `0-1(y, t) = 1(t 6= y) `0-1(y, t) = 1(t 6= y) Surrogate loss ψ : {±1} × C 7→ R+ ψ : [n]× C 7→ R+ ψ : Y × C 7→ R+ where C ⊆ R where C ⊆ R ‘pred’ function pred : C 7→ {±1} pred : C 7→ [n] pred : C 7→ T pred(α) = sign(α) p...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
عنوان ژورنال:
دوره شماره
صفحات -
تاریخ انتشار 2005