A Survey Paper on Error

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. Lately, the ECOC framework was extended from the binary to the tern ignored by a certain classifier, allowing in this way to increase the number of possible dichotomies to be selected. we show that by a special treatment procedure of zeros, and adjusting the weights at the rest of coded posi accuracy of the system can be increased. Besides, we extend the main state binary to the ternary case, and use two novel approaches: Laplacian and Pessimistic Beta Density Probability approaches. Our main research is to show that the ternary decoding techniques proposed outperform the standard decoding strategies. In this survey paper, we present an open source Error The ECOC framework is a powerful tool to deal with multiclass categorization problems. This library contains both state-of-the-art coding (one-versus-one, one ECOC-ONE) and decoding designs (hamming, based, probabilistic kernel-based, and loss option to include your own coding, decoding, and base