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Consider the data with categorical predictor x1 = {green or red} and numerical predictor x2 and the class variable y shown in the following table. The weights for a round of boosting are also shown in the table. Suppose the classifier built from this round of boosting assigns y = 1 for x2 < 0.45 and y = -1 for x2 > 0.45. Compute the coefficient α for this classifier in the adaboost algorithm.
Functioning of ROLAP happens concurrently with relational databases. Data and tables are stored as relational tables. To hold new information or data new tables are formed. Functio
A relation schema R is in BCNF with respect to a set F of functional dependencies if for all functional dependencies in F+ of the form a->b, where a and b is a subset of R, at leas
Q. Explain Passing Parameters in General Memory? The parameters can be passed in the memory too. In such a technique name of the memory location is used as a parameter. The res
The code where all successive numbers differ from their preceding number by single bit is ? Ans. Gray Code, where all successive numbers are different from their preceding nu
Construct a shift register from S-R flip-flops. Explain its working. Ans: S-R Flip-Flop Shift Register: Shift registers can be built through using SR flip-flops. Fig.(a)
Somehow, the worksheet has external links to files that are no longer around. How can we delete the links? Ans) This happens all of the time to MrExcel. I downloaded a little
Q. Explain the Memory Transfer process? Memory Transfer Transfer of information from memory to outside world which implies I/O Interface is known as a read operation. Tra
What are the steps comprised in authentication? Steps in Authentication: The control over the access of the resources within the repository is exercised in two steps tha
Q. Example on PUBLIC DIVISOR? Linker appends all segments having the same name and PUBLIC directive with segment name into one segment. Their contents are pulled together in co
The major programs here are Arthur Samuel's, the rote learning method which is a lot like a memory based method, generalization learning which is a lot like backprop and a signatur
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