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 class...
— As the complexity of SoCs is increasing, hardware/software co-verification becomes an important part of system verification. C-level cycle-based simulation could be an efficien...
A method for tuning MLP learning parameters in an ensemble classifier framework is presented. No validation set or cross-validation technique is required to optimize parameters for...
Functional simulation is the most widely used method for design verification. At various levels of abstraction, e.g., behavioral, register-transfer level and gate level, the design...
AdaBoost.OC has shown to be an effective method in boosting "weak" binary classifiers for multi-class learning. It employs the Error Correcting Output Code (ECOC) method...