Multiclass learning problems involve nding a de nition for an unknown function f(x) whose range is a discrete set containing k > 2 values (i.e., k \classes"). The de nition is acquired by studying collections of training examples of the form hxi; f(xi)i. Existing approaches to multiclass learning problems include direct application of multiclass algorithms such as the decision-tree algorithms C...

In this paper, we present a new biometric verification system. The proposed system employs a novel biometric hashing scheme that uses our proposed quantization method. The proposed quantization method is based on error-correcting output codes which are used for classification problems in the literature. We improve the performance of the random projection based biometric hashing scheme proposed ...

When a sample belongs to more than one label from a set of available classes, the classification problem (known as multi-label classification) turns to be more complicated. Text data, widely available nowadays in the world wide web, is an obvious instance example of such a task. This paper presents a new method for multi-label text categorization created by modifying the Error-Correcting Output...