Though deep learning has been applied successfully in many scenarios, malicious inputs with human-imperceptible perturbations can make it vulnerable real applications. This paper proposes an error-correcting neural network (ECNN) that combines a set of binary classifiers to combat adversarial examples the multi-class classification problem. To build ECNN, we propose design code matrix so minimu...

This paper explores in detail the use of Error Correcting Output Coding (ECOC) for learning text classifiers. We show that the accuracy of a Naive Bayes Classifier over text classification tasks can be significantly improved by taking advantage of the error-correcting properties of the code. We also explore the use of different kinds of codes, namely Error-Correcting Codes, Random Codes, and Do...

A distance-based conditional model on the ranking poset is presented for use in classification and ranking. The model is an extension of the Mallows model, and generalizes the classifier combination methods used by several ensemble learning algorithms, including error correcting output codes, discrete AdaBoost, logistic regression and cranking. The algebraic structure of the ranking poset leads...

This paper generalizes parallel error correcting codes proposed by Ahlswede et al. over a new type of multiple access channel called parallel error channel. The generalized parallel error correcting codes can handle with more errors compared with the original ones. We show construction methods of independent and non-independent parallel error correcting codes and decoding methods. We derive som...

Error correcting output codes (ECOC) represent a successful extension of binary classifiers to address the multiclass problem. In this paper, we propose a novel technique called ECOCONE (Optimal Node Embedding) to improve an initial ECOC configuration defining a strategy to create new dichotomies and improve optimally the performance. The process of searching for new dichotomies is guided by th...

Novel arti cial intelligence methods are used to classify 16x16 pixel regions (obtained from Advanced Very High Resolution Radiometer (AVHRR) images) in terms of cloud type (e.g., stratus, cumulus, etc.). We previously reported that intelligent feature selection methods, combined with nearest neighbor classi ers, can dramatically improve classi cation accuracy on this task. Our subsequent analy...