3.2 Can the network learn all by itself?
(translation by Agata Krawcewicz, hogcia@gmail.com)
Close by to the previously described schema of the learning with a tutor is also a series of
methods of the so called learning without a teacher (or self-learning networks) that are in use.
These methods consist of passing only a series of example input data to the entries of
networks, without giving any information concerning desirable or even only anticipated
output signals. It seems that the properly designed neural network can use only observations
of entrance-signals and build the sensible algorithm of its own activity on their base - most
often relying on the fact, that classes of repeated (maybe with certain variety) input signals are
automatically detected and the network learns (completely spontaneously, without any open
learning) to recognize these typical patterns of signals.
With the self-learning networks you must also have the learning set – only that this set will
only consist of data provided for input of the network. There is no output data given, because
the technique of the learning without the teacher we should apply in the situation, when we do
not know, what to demand from the network analysing some data. If we, for example, take
data shown in fig. 3.1, to the learning without a teacher, you would only use columns
described as input data, instead of giving the information from the column noted with the red
pointer to the network. Network which would gain knowledge in such process of the learning
would not have chances on predicting, in which city the air pollution will be greater, and
wherein smaller, because it will not gain such knowledge itself. However analyzing the data
on the subject of different cities the network may for example favor (just by itself!) a group of
large industrial cities and learn to differentiate these cities from small country towns being
centers of agricultural regions. This distinction will surrender to deducting from given input
data on such a rule that industrial towns are mutually similar, whereas agricultural cities also
have many common characteristics. On a similar rule the network can, only by itself, separate
cities with beautiful and bad weather and make many other classifications, based only on
values of observed input data.
Notice that the self-learning network is very interesting from the point of view of the analogy,
which exist between suchlike activity of the networks and with the activity of the human
brain: people also have an ability of spontaneous classifying of encountered objects and
phenomena (some call this "a formation of notions"), and after the execution of the suitable
classification, recognizes another objects as adherent to one of these earlier recognized
classes. Self-learning is also very interesting from the point of view of its uses, because it
demands no openly given external knowledge - which can be inaccessible or gathering which
can be too troubling - the network will accumulate all necessary information and pieces of
news all by itself. In one of the further chapters I will describe very exactly (and I will show
demonstratively across suitable programs!) what is this self-learning of the networks based
on.
Now you can imagine (rather for fun and stimulation of the imagination, than from a real
need) that a self-learning network with TV camera can be sent in the unmanned space probe
on Mars. We do not know, what are the conditions on Mars, so we do not know, which
objects our probe should recognize. What is more - we do not even know how many classes
of objects will appear! It does not matter, the network will cope with it itself (look at fig. 3.2).
The probe lands and the network begins the process of self-learning. At the beginning it
recognizes nothing, only observes. However, over time the process of the spontaneous self-
organization will lead to a situation that the network will learn to detect and to differentiate
between different types of input signals which appear on its entry: separately rocks and
stones, separately plant forms (if they will be there), and separately living organisms. If we
will give the network sufficient amount of time - it can be so educated that it can differentiate
Martian-men from Martian-women - though her creator did not even know that they exist!
Fig. 3.2.
Fig. 3.2. Hypothetic planetary lander powered with self-learning neural network can discover
not known forms of life (“alien”) on mysterious planets
Of course earlier described self-learning Mars lander is exclusively a hypothetical creation,
even though networks that form and recognize different patterns exist and are eagerly used.
For example we could be interested in the fact of how many and which forms of the certain
little known disease can in fact be found. Is this one sickness unit, or several? What are they
differed by? How to cure them?
It will be sufficient to take a self-learning neural network and show it the information on
registered patients and their symptoms for a long enough period of time. Sometime later the
network will give the information on how many typical groups of symptoms and signs will be
detected and on the ground of which criteria one can differentiate patients classified to
different groups. Explorer then only has to name these groups with properly wise sounding
Latin names of illnesses - and he can already run to the tailor, to try on the tailcoat sewn for
the solemnity of the handing of Nobel Prize!
The described method of self-learning has of course (as everything on this world)
definite defects - however, I will describe them a little later, when it will be already clear how
all this works. Nevertheless, it has so many undeniable advantages, that one ought to be
surprised with its comparatively small popularity!