Data-Dependent Generalization Bounds for Multi-Class Classification
نویسندگان
چکیده
منابع مشابه
Generalization Error Bounds for Extreme Multi-class Classification
In this paper, we study data-dependent generalization error bounds exhibiting a mild dependency on the number of classes, making them suitable for multi-class learning with a large number of label classes. The bounds generally hold for empirical multi-class risk minimization algorithms using an arbitrary norm as regularizer. Key to our analysis are new structural results for multiclass Gaussian...
متن کاملData-Dependent Margin-Based Generalization Bounds for Classification
We derive new margin-based inequalities for the probability of error of classifiers. The main feature of these bounds is that they can be calculated using the training data and therefore may be effectively used for model selection purposes. In particular, the bounds involve empirical complexities measured on the training data (such as the empirical fatshattering dimension) as opposed to their w...
متن کاملGeneralization error for multi-class margin classification
In this article, we study rates of convergence of the generalization error of multi-class margin classifiers. In particular, we develop an upper bound theory quantifying the generalization error of various large margin classifiers. The theory permits a treatment of general margin losses, convex or nonconvex, in presence or absence of a dominating class. Three main results are established. First...
متن کاملMulti-class SVMs: From Tighter Data-Dependent Generalization Bounds to Novel Algorithms
This paper studies the generalization performance of multi-class classification algorithms, for which we obtain—for the first time—a data-dependent generalization error bound with a logarithmic dependence on the class size, substantially improving the state-of-the-art linear dependence in the existing data-dependent generalization analysis. The theoretical analysis motivates us to introduce a n...
متن کاملData-driven decomposition for multi-class classification
This paper presents a new study on a method of designing a multi-class classifier: Data-driven Error Correcting Output Coding (DECOC). DECOC is based on the principle of Error Correcting Output Coding (ECOC), which uses a code matrix to decompose a multi-class problem into multiple binary problems. ECOC for multi-class classification hinges on the design of the code matrix. We propose to explor...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Transactions on Information Theory
سال: 2019
ISSN: 0018-9448,1557-9654
DOI: 10.1109/tit.2019.2893916