Introduction to Algorithms
3rd Edition
ISBN: 9780262033848
Author: Thomas H. Cormen, Ronald L. Rivest, Charles E. Leiserson, Clifford Stein
Publisher: MIT Press
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Chapter 5.3, Problem 1E
Program Plan Intro
To rewrite the procedure RANDOMIZE-IN-PLACE so that the corresponding loop invariants holds to a nonempty subarray prior to first equation.
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how to use a histrogram to estimate the the size of selection of the form σA<=γ(r)?
Appendix A
10-Fold Cross Validation for Parameter Selection
Cross Validation is the standard method for evaluation in empirical machine learning. It can also be used
for parameter selection if we make sure to use the training set only.
To select parameter A of algorithm A(X) over an enumerated range d E [A1,..., A] using dataset D, we do
the following:
1. Split the data D into 10 disjoint folds.
2. For each value of A e (A1,..., Ar]:
(a) For i = 1 to 10
Train A(A) on all folds but ith fold
Test on ith fold and record the error on fold i
(b) Compute the average performance of A on the 10 folds.
3. Pick the value of A with the best average performance
Now, in the above, D only includes the training data and the parameter A is chosen without the knowledge
of the test data. We then re-train on the entire train set D using the chosen A and evaluate the result on
the test set.
Q4// Simplify and then implement the Boolean
function F(A,B,C,D) = N (1,2,3,5,6,7,9,11,13,15)
using POS karnough map , draw the
implementation.
%3D
Chapter 5 Solutions
Introduction to Algorithms
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