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Inductive Logic Programming :
Now here having studied a non-symbolic approach to machine learning on Artificial Neural Networks then we return to a logical approach, namely Inductive Logic Programming is as "ILP". Thus there as the name suggests that the representation scheme required in this approach is logic programs that we covered in lecture 6. So as a quick overview there is one search strategy for "ILP" systems is to invert rules of deduction and therefore induce hypotheses that may solve the learning problem.
Conversely in order to understand "ILP" there we will define a context for ‘ILP' and then need this to state the machine learning problem being addressed. However following this we will look at the search operators in ‘ILP', in particularly the notion of inverting resolution in order to generate hypotheses. Thus we will judge that how the search is undertaken and run through a session regarding the Progol ILP system. So here we end through looking at some of the applications of "Inductive Logic Programming".
Linear Equalizer and Decision Feedback Equalizer Simulation and Performance Comparison In this project , performance of decision feedback equalizer is analyzed,
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