ARTIFICIAL INTELLIGENCE

Znanstvena založba Nova Science Publishers  iz New Yorka je nedavno izdala knjigo z naslovom  Advances in Machine Learning Research (Napredek v raziskavah strojnega učenja) . Tretje poglavje z naslovom    Optimization for Multi-Layer Perceptron: Without the Gradient , je  plod slovenske znanosti in   opisuje  dve novi metodi strojnega učenja. Obe sta nadgradnja zelo  uveljavljene metode Backpropagation, ki je temelj delovanja nevronskih mrež, ki so ene od najbolj razširjenih naprav na področju umetne inteligence. Novi metodi pomenita velik napredek, saj odpravljata ozko grlo umetne inteligence - izboljšujeta potek in rezultat učenja.    Prva metod se imenuje Bipropagation in je majnša izboljšava, ki omogoča mnogo hitrejše in bolj zanesljivo učenje. Druga metoda -  Metoda mejnih parov   (angleško Border Pairs Method, BPM)  je povsem izvirna   in ima številne prednosti pred metodo Backpropagation: samodejno najde primerno zgradbo nevronske mreže (MLP), vedno najde rešitev,

  These days, the publishing house Nova Publishers published the book, entitled Advances in Machine Learning Research . In it is a chapter entitled OPTIMIZATION FOR MULTI LAYER PERCEPTRON: WITHOUT THE GRADIENT where I describe two new algorithms for neural networks learning ( Bipropagation and Border Pairs Method ). Both of them are much more powerful than their predecessors - Backpropagation algorithm. The second algorithm is among other things constructive. Abstract of the book chapter During the last twenty years, gradient-based methods have been primarily focused on the Feed Forward Artificial Neural Network learning field. They are the derivatives of Backpropagation method with various deficiencies. Some of these include an inability to: cluster and reduce noise, quantify data quality information, redundant learning data elimination. Other potential areas for improvement have been identified; including, random initialization of values of free parameters, dynam