TY - JOUR
AU - Badr, Assem
AB - For a better future in machine learning (ML), it is necessary to modify our current concepts to get the fastest ML. Many designers had attempted to find the optimal learning rates in their applications through many algorithms over the past decades, but they have not yet achieved their target of highest speed of back-propagation (BP). This research proposes a novel BP rule called the Instant Learning Ratios-Machine Learning (ILR-ML) or (ILRML). Unlike the traditional BP algorithms, the ILR-ML offers its learning without the concepts of the learning rate(s). The ILR-ML has a new concept called the "Learning Ratio" and indicated by a sign (Δℓ). The ILR-ML performs the full BP algorithm with 100% accuracy per each learning iteration. The ILR-ML is more suitable for the online machine learning.
TI - Awesome back-propagation machine learning paradigm
JF - Neural Computing and Applications
DO - 10.1007/s00521-021-05951-6
DA - 2021-10-01
UR - https://www.deepdyve.com/lp/springer-journals/awesome-back-propagation-machine-learning-paradigm-xFUfiikDAV
SP - 13225
EP - 13249
VL - 33
IS - 20
DP - DeepDyve
ER -