Strani

ponedeljek, 30. oktober 2017

A new Deep Learning Algorithm: One-Step Method



We are living in the AI era where progres is faster and faster each and every single day. Here is another one discovery in this field: One Step Method, a new machine learning algorithm which can do many things, amongst other can replace digital circuits with neurons, can find the even better construction of neural network than Border Pairs Method. More you can find in the 3rd chapter of our book: Machine Learning: Advances in Research and Applications from Nova Science Publishers.




This new algorithm is also suitable for Deep Learning in combination with other methods like convolutional learning, bipropagation, border pairs method, autoencoder and others.

ponedeljek, 23. oktober 2017

Barcelona 2017






This September in Barcelona was very interesting. The city alone is beautiful people are very kind. Memory on the terrorist attack on the Rambla was still alive:



Spirit of the oncoming referendum was already present, flags of  Catalunya were everywhere. But The Heartbeat of tourism was not disturbed at all:


A nice dinner in the theatre:


Oh, let's not forget it was a business trip:






sreda, 07. december 2016

Video: Two Deep Learning algorithms

  • Online web session (second half of  video, after 32 minutes of intro)


  • ABSTRACT

    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.

    SPEAKER 

    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)
    Research
       Voice recognition with NN
       Hexapod gait control with NN
       Bipropagation algorithm for learning NN
       Border pairs method for learning NN 

ponedeljek, 14. november 2016

Bipropagation demo in TensorFlow

Rezultat iskanja slik za neural net


Bipropagation is new Deep Learning algorithm. It is much faster and much more reliable than Backpropagation. Here is the demo at ResearGate and GitHub. Inner layers are not hidden anymore, learning is done layer by layer. Please cite me in your work.

Any comments are desirable.

nedelja, 16. oktober 2016

Video prikaz dveh novih metod strojnega učenja, ki zasenčita dosedanjo metodo






Rezultat iskanja slik za ijs is2016


Raziskovalci se občasno sestanemo, da drug drugemu predstavimo svoje delo, svoje dosežke. Dobra priložnost za to so znanstvene konference. Ena naj bolj uglednih tovrstnih konferenc na področju umetne inteligence v Sloveniji z naslovom Informacijska družba 2016, se je odvila te dni na institutu Jožef Štefan v Ljubljani.

Rezultat iskanja slik za ijs is2016

Na njej smo obravnavali mnogo zanimivih tem, ki nam nakazujejo smer razvoja informacijske tehnologije v družbi v bližnji prihodnosti. Zadnja leta je vedno bolj v ospredju umetna inteligenca in strojno učenje, trenutno v svetu še posebej odmevajo dosežki globokega učenja. Nekatere druge teme so bile: evidentiranje genetsko modificiranih organizmov v živilih, zaznavanje stresa v službi, priporočanje čtiva, odkrivanje novih zlitin, sinteza slovenskega govora, razvoj avtonomnega vozila; seveda vse skupaj na osnovi umetne inteligence. Sam sem predstavil kolegom dva nova algoritma strojnega učenja, ki sem ju razvil v bližnji preteklosti. Več o vsebini si lahko ogledate v zborniku. Razen vsebine prispevkov, pa je na konferenci pomembno tudi navezovanje stikov s kolegi in izmenjava mnenj z njimi.

Video





sreda, 24. avgust 2016

Beyond Backpropagation


Advances in Machine Learning Research



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".


Bipropagation algorithm is still iterative like a "backpropagation", but internal layers are not hidden anymore since their desired values are now calculated in advance before learning. That way can machine learning be conducted layer by layer, what represents great improvement (it could be more than a few ten times faster).


"Border pairs method" is a totally new algorithm which has many advantages over "backpropagation" algorithm. Firstly we look for the pairs of patterns of opposite class which are so near, that no third pattern does lie in between them. This are the Border pairs and only this patterns are significant when we want to draw a borderline between classes. So the majority of learning patterns (sometimes more than 90%) is eliminated even before the learning begins. Then we are trying to separate all border pairs with a minimal number of border lines, which represent neurons of the 1st layer, so we find the minimal structure of the neural network. While we draw one by one borderline, the learning of the first layer is done with only one neuron at the same time. Since these neurons have a hard limiter, they put binary output values and so the next layers could even be replaced with a logical circuit. Border pairs also allow a simple reduction of the noise.


More info 

So it is time for a change.

petek, 01. julij 2016

Umetna inteligenca, odločitvena drevesa, nevronske mreže in mehka logika





Rezultat iskanja slik za elektrotehniška revijaPred kratkim sem dobil od urednika Elektrotehniške revije, g. Ervina Sršena, vabilo za objavo članka, ki se nanaša na moje raziskovalno področje - umetno inteligenco. Takšna čast te ne doleti vsak dan, zato sem se z veseljem odzval in se lotil pisanja. Z urednikom sva se dogovorila, da bo članek obsegal več nadaljevanj.  Prvo nadaljevanje govori o umetni inteligenci na splošno. Drugo nadaljevane govori o odločitvenih drevesih . V tretjem nadaljevanju so predstavljene nevronske mreže, v četrtem pa mehki asociativni pomnilnik. V pripravi pa je še eno nadaljevanje o genetskih algoritmih. Dragi bralec zanima me tvoje mnenje, ki ga lahko podaš v komentarju spodaj.