we present an approach to design of fault tolerant computing systems. in this paper, a technique is employed that enable the combination of several codes, in order to obtain flexibility in the design of error correcting codes. code combining techniques are very effective, which one of these codes are turbo codes. the algorithm-based fault tolerance techniques that to detect errors rely on the c...

This article proposes a general extension of the error correcting output codes framework to the online learning scenario. As a result, the final classifier handles the addition of new classes independently of the base classifier used. In particular, this extension supports the use of both online example incremental and batch classifiers as base learners. The extension of the traditional problem...

We present a reduction from cost sensitive classi cation to binary classi cation based on (a modi cation of) error correcting output codes. The reduction satis es the property that regret for binary classi cation implies l2-regret of at most 2 for cost-estimation. This has several implications: 1) Any regret-minimizing online algorithm for 0/1 loss is (via the reduction) a regret-minimizing onl...

Existing deep networks are generally initialized with unsupervised methods, such as random assignments and greedy layerwise pre-training. This may result in the whole training process (initialization/pre-training + fine-tuning) to be very timeconsuming. In this paper, we combine the ideas of ensemble learning and deep learning, and present a novel deep learning framework called deep error-corre...

One of the most widely applied techniques to deal with multiclass categorization problems is the pairwise voting procedure. Recently, this classical approach has been embedded in the Error-Correcting Output Codes framework (ECOC). This framework is based on a coding step, where a set of binary problems are learnt and coded in a matrix, and a decoding step, where a new sample is tested and class...

Sensitive error correcting output codes are a reduction from cost sensitive classi cation to binary classi cation. They are a modi cation of error correcting output codes [3] which satisfy an additional property: regret for binary classi cation implies at most 2 l2 regret for cost-estimation. This has several implications: 1) Any 0/1 regret minimizing online algorithm is (via the reduction) a r...

Error-correcting output codes (ECOC) are a successful technique to combine a set of binary classifiers for multi-class learning problems. However, in traditional ECOC framework, all the base classifiers are trained independently according to the defined ECOC matrix. In this paper, we reformulate the ECOC models from the perspective of multi-task learning, where the binary classifiers are learne...