Already have an account? Get multiple benefits of using own account!
Login in your account..!
Remember me
Don't have an account? Create your account in less than a minutes,
Forgot password? how can I recover my password now!
Enter right registered email to receive password!
Pruning and Sorting:
This means we can test where each hypothesis explains as entails a common example that we can associate to a hypothesis a set of positive elements in which it explains and a similar set of negative elements. Moreover there is also a similar analogy with general and specific hypotheses as described above as: whether a hypothesis G is more practical than hypothesis S so then the examples explained by S will be a subset of those explained by G.
In fact we will assume the following generic search strategy for an ILP system as: (i) is a set of current hypotheses is maintained and QH (ii) is at each step in the search, a hypothesis H is taken from QH and some inference rules applied to it in order to generate some new hypotheses that are then added to the set as we say that H has been expanded (iii) is, this continues until a termination criteria is met. However this leaves many questions unanswered. By looking first at the question of that hypothesis to expand at a particular stage, ILP systems associate a label with each hypothesis generated that expresses a probability of the hypothesis holding which is given the background knowledge and examples are true. After then there hypotheses with a higher probability are expanded rather than those with a lower probability and hypotheses with zero probability are pruned from the set QH entirely. However this probability calculation is derived using Bayesian mathematics and we do not go into the derivation here. Moreover we hint at two aspects of the calculation in the paragraphs below.
In just specific to general ILP systems there the inference rules are inductive so each operator takes a hypothesis and generalizes it. However as mentioned above that this means like the hypothesis generated will explain more examples than the original hypothesis. In fact as the search gradually makes hypotheses more generally there will come a stage where a newly formed hypothesis H is common enough to explain a negative example as e- . Thus this should therefore score zero for the probability calculation is just because it cannot possibly hold given the background and examples being true. This means the operators only generalize so there is no way through H can be fixed to not explain e-, so pruning it from QH means the zero probability score is a good decision.
Problem: a) Authoring tools consist of two basic features. First, an authoring facility for creating and editing, and second, a presentation vehicle for delivery. The authorin
Microsoft in the late 1997 introduced a standard API called as OLE DB. After which XML was used for analysis specification and this specification was largely used by a lot of vendo
What are the various functional verification methodologies Ans: TLM (Transaction Level Modelling) Linting RTL Simulation (Environment involving : stimulus generators,
The decimal equivalent of Binary number 10101 is ? Ans. 1x2 4 + 0x2 3 +1x2 2 +0x2 1 + 1x2 0 = 16 + 0 + 4 + 0 + 1 = 21.
Define Multiprogramming. Multiprogramming: A multiprogramming operating system is system which allows extra than one active user program or part of user program to be store
What are the basic approaches to the design of subscriber access to Strowger systems? Describe them. A step by step switching system has three main parts as demonstrated in fig
Dear, I''m an engineering post graduate in computer science. I would like to work as online tutor. please suggest ideas. Thank You.
Sometime you may have to reset your computer (i.e., Reboot DOS) when it is still running because DOS does not work accurately. To reset your computer you have two choices: 1. P
What is meant by stacked list? A stacked list is nothing but secondary list and is showed on a full-size screen unless you have specified its coordinates using the window comm
Broad Band ISDN handles data rate of about (A) 64 kbps (B) 100 mbps (C) 5.4 mbps (D) 2.048 mbps
Get guaranteed satisfaction & time on delivery in every assignment order you paid with us! We ensure premium quality solution document along with free turntin report!
whatsapp: +91-977-207-8620
Phone: +91-977-207-8620
Email: [email protected]
All rights reserved! Copyrights ©2019-2020 ExpertsMind IT Educational Pvt Ltd