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 ...

This paper proposes a novel feature extraction method based on ensemble learning. Using the errorcorrecting output codes (ECOC) to design binary classifiers (dichotomizers) for separating subsets of classes, the outputs of the dichotomizers are linear or nonlinear features that provide powerful separability in a new space. In this space, the vector quantization based meta classifier can be view...

The construction of optimal linear block errorcorrecting codes is not an easy problem, for this, many studies describe methods for generating good. That is, even when taking into account the multitude of errors possible for multilevel quantum systems, topological quantum error-correction codes employing. The point of error correcting codes is to control the error rate. Let's say you have a Sing...

Error-correcting codes and related combinatorial constructs play an important role in several recent (and old) results in computational complexity theory. In this paper we survey results on locally-testable and locally-decodable error-correcting codes, and their applications to complexity theory and to cryptography. Locally decodable codes are error-correcting codes with sub-linear time error-c...