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classical approach - canonical genetic algorithmhowever returning to the classical approach as there example whether solving a particular problem
canonical genetic algorithmin such a scenario with all search techniques there one of the first questions to ask along with gas is how to define a
evolutionary approaches boil down - artificial intelligencein fact as we will see whether evolutionary approaches boil down to like i just to
genetic algorithmsin such a scenario the evolutionary approach to artificial intelligence is one of the neatest ideas of all whether we have tried to
variable ordering - forward checkinghence this is different from variable ordering in two important ways as whether this is a dead end when we
fail-first - artificial intelligencealternatively one such dynamic ordering procedure is known like fail-first forward checking in fact the idea is
demonstrate arc consistencyto demonstrate the worth of performing an arc-consistency check before starting a serarch for a solution well use an
arc consistencythere have been many advances in how constraint solvers search for solutions remember this means an assignment of a value to each
optimum solution based on constraint problemswhether depending on what solver you are using so there constraints are often expressed as relationships
specifying constraint problemshowever as with most successful ai techniques there constraint solving is all about solving problems as somehow phrase
constraint satisfaction problemsfurthermore i was perhaps most proud of ai on a sunday however this particular sunday a friend of mine found an
appropriate problems for ann learningconversely as we did for decision trees there its important to know where anns are the right representation
overfitted the datamoreover notice that as time permitting it is worth giving the training algorithm the benefit of the doubt as more as possible
local minima - sigmoid unitsalternatively in addition to getting over some local minima where the gradient is constant in one direction or adding
adding momentum - sigmoid unitshowever imagine a ball rolling down a hill as it does so then it gains momentum in which its speed increases and it
backpropagationhowever backpropagation can be seen as utilising searching a space of network configurations as weights in order to find a
backpropagation learning routineconversely as with perceptrons there the information in the network is stored in the weights than the learning
solution of multi-layer ann with sigmoid unitsassume here that we input the values 10 30 20 with the three input units and from top to bottom so
learning abilities of perceptronsconversely computational learning theory is the study of what concepts particular learning schemes as representation
learning algorithm for multi-layered networksfurthermore details we see that if s is too high the contribution from wi xi is reduced it means
perceptron traininghere the weights are initially assigned randomly and training examples are needed one after another to tweak the weights in the
units of artificial neural networkshowever the input units simply output the value that was input to them from the example to be propagated so every
perceptronshowever the weights in any ann are usually just real numbers and the learning problem boils down to choosing the best value for each
architecture of artificial neural networkspresumably artificial neural networks consist of a number of units that are mini calculation devices but
artificial neural networkshowever imagine now in this example as the inputs to our function were arrays of pixels and there actually taken from