In this video conference, two new algorithms for learning Feed-Forward Artificial Neural Network are presented. In the introduction, a brief description of the development of the existing algorithms and their flaws are shown. The second part describes the first new algorithm - Bipropagation. The basic idea is given first, followed by a detailed description of the algorithm. In the third part yet another new algorithm is given, called Border Pairs Method. Again is first given a basic idea and then follows a detailed description of the algorithm. In the fourth part, the results and findings of experimental work are presented. In the conclusion, it is found that two described algorithms are fast and reliable - the second one is also constructive.
Bojan PLOJ, PhD
Born 1965 in Maribor, Slovenia, Europe
Thesis Border Pairs Method for learning of neural network
Job 1 year R&D engineer at Birostroj Computers
10 years teaching at Electronics high school in Ptuj
4 years assistant professor University of Maribor
7 years lecturer at Higher vocational college Ptuj
3 years lecturer at the college of Ptuj (Artificial intelligence)
Voice recognition with NN
Hexapod gait control with NN
Bipropagation algorithm for learning NN
Border pairs method for learning NN
Bipropagation is a new Deep Learning algorithm. It is much faster and much more reliable than Backpropagation. Here is the demo from the ResearchGate and GitHub. Inner layers of the Neural Network have not hidden anymore. Learning is done layer by layer with much fewer iterations. Please cite me in your work.
Click the G+button if you like this demo. Any comments are desirable.
Gartner is predicting a very bright near future for the "Machine learning". 2015 was a peak year of inflated expectations, now, in 2016 is following period of disillusionment and in 2017 should be reached the plateau of productivity. Elsewhere this process usually last for 10 years. One kind of the most popular modern "machine learning" is named "Deep Learning" what is another name for neural networks with little bit more layers and perhaps even with a convolution and/or recursion. The learning of this kinds networks was until now usually based on gradient descent, on slow, iterative, non-reliable process named Backpropagation. That kind of learning is very demanding and extensive. On plain computer can last for hours or even many days and is often unsuccessful concluded. Recently are appeared two algorithms that significantly improve this kind of machine learning: "Bipropagation" and "Border pairs method".
Ko sem med raziskovanjem za potrebe podiplomskega študija dobil idejo za nov algoritem strojnega učenja, me je prevzel notranji nemir. Zaslutil sem, da sem na sledi pomembnega odkritja in v hipu sem začutil kako se mi po žilah pretaka adrenalin. Pravijo, da je raziskovalna strast lahko večja celo od tiste hazarderske, ki je menda zakrivila številne zgodbe iz črne kronike. No, na vso srečo pa raziskovalna strast ni povezana s tako nizkotnimi pobudami kot hazarderska. Ideji algoritma je nato sledil njegov razvoj, ki je trajal več kot leto in je bil prežet s številnimi vzponi in padci. Navidezne težavice so pogosto preraščale v težave, a na srečo se je vedno našla rešitev za njih. V meni sta se tako prepletala dvom in radost, dokler eksperimenti niso potrdili vseh mojih pričakovanj. Takrat so me preplavili prijetni občutki vznesenosti, ki bi jih lahko primerjali z nekakšno zaljubljenostjo. Ko si vznesen si stvarnost slikaš lepšo, kot je v resnici in tako sem naivno pričakoval, da bo s…