Machine learning for decoding linear block codes: case of multi-class logistic regression model

<p>Facing the challenge of enormous data sets variety, several machine learning-based algorithms for prediction (e.g, Support vector machine, multi layer perceptron and logistic regression) have been highly proposed used over last years in many fields. Error correcting codes (ECCs) are extensively practice to protect against damaged storage systems random errors due noise effects. In this paper, we will use learning methods, especially multi-class regression combined with famous syndrome decoding algorithm. The main idea behind our method which call decoder (LRDec) is efficient models find from syndromes linear such as bose, ray-chaudhuri hocquenghem (BCH), quadratic residue (QR). Obtained results a significant benefit terms bit error rate (BER) binary codes. comparison competitors proves its power. has reached success percentage 100% correctable studied codes.</p>