We study an approach for speeding up the training of error-correcting output codes (ECOC) classifiers. The key idea is to avoid unnecessary computations by exploiting the overlap of the different training sets in the ECOC ensemble. Instead of re-training each classifier from scratch, classifiers that have been trained for one task can be adapted to related tasks in the ensemble. The crucial iss...

We study an approach for speeding up the training of error-correcting output codes (ECOC) classifiers. The key idea is to avoid unnecessary computations by exploiting the overlap of the different training sets in the ECOC ensemble. Instead of re-training each classifier from scratch, classifiers that have been trained for one task can be adapted to related tasks in the ensemble. The crucial iss...

List Decoding and Property Testing of Error Correcting Codes Atri Rudra Chair of the Supervisory Committee: Associate Professor Venkatesan Guruswami Department of Computer Science and Engineering Error correcting codes systematically introduce redundancy into data so that the original information can be recovered when parts of the redundant data are corrupted. Error correcting codes are used ub...

Fungal infection is a pre-harvest and post-harvest crisis for farmers of peanuts. In environments with temperatures around 28 °C to 30 or relative humidity approximately 90%, mold-contaminated peanuts have considerable likelihood be infected Aflatoxins. Aflatoxins are known highly carcinogenic, posing danger humans livestock. this work, we proposed new approach detection at an early stage. The ...

Please cite this article in press as: Hatami, N. doi:10.1016/j.eswa.2011.07.091 Error-correcting output coding (ECOC) is a strategy to create classifier ensembles which reduces a multiclass problem into some binary sub-problems. A key issue in designing any ECOC classifier refers to defining optimal codematrix having maximum discrimination power and minimum number of columns. This paper propose...

Accurate classification of dialog acts (DAs) is important for many spoken language applications. Different methods have been proposed such as hidden Markov models (HMM), maximum entropy (Maxent), graphical models, and support vector machines (SVMs). In this paper, we investigate using SVMs for multiclass DA classification in the ICSI meeting corpus. We evaluate (1) representing DA tagging direc...