CSCE 478/878 (Fall 2014) Homework 2
Assigned Tuesday, October 7
Tuesday, October 21
Thursday, October 23
at 11:59 p.m.
When you hand in your results from this homework,
you should submit the following, in separate
- A single zip file called username.zip, where username is
your username on cse. In this file, put:
- Source code in in the language of your choice (in plain text files).
- A makefile and/or a README.txt file facilitating compilation and running
of your code (include a description of command line options).
If we cannot easily re-create your experiments, you might not get full
- All your data and results (in plain text files).
- A single .pdf file with your writeup of the results for all the
homework problems, including the last problem.
Only pdf will be accepted, and you should
only submit one pdf
file, with the name username.pdf, where username is
your username on cse. Include all your plots in this file, as
well as a detailed summary of your experimental setup, results,
and conclusions. If you have several plots, you might put a few
example ones in the main text and defer the rest to an appendix.
Remember that the quality of your writeup strongly affects your grade.
See the web page on
on Presenting Technical Material''.
Submit everything by the
due date and time using the
web-based handin program.
On this homework, you must work on your own and submit your own results written
in your own words.
- (20 pts)
Design a two-input perceptron that implements the boolean function A ∧ [∼
B], where ∼ is logical negation. Design a multi-layer network of perceptrons to
implement [A ⊕ B] ⊕ C, where ⊕ represents exclusive OR.
- (15 pts)
Suppose a hypothesis commits 10 errors over a sample of 65 independently drawn test examples.
What is the 90% two-sided confidence interval for the true error rate?
What is the 95% one-sided interval?
What is the 90% one-sided interval?
- (85 pts) Implement
an artificial neural network (ANN) with at least one hidden layer.
You may hard-code the sizes of the input and hidden layers, or you may set them
dynamically based on parameters passed to the program.
Your ANN will be trained by the Backpropagation algorithm.
If you use discrete-valued attributes or multiclass labels, explain in your
report how you implemented that in your ANN.
You are to compare your ANN's results to those from ID3 on the
same UCI data sets you used for Homework 1 (if you were unsuccessful in getting
your ID3 implementation working, you may utilize an existing implementation,
such as Weka's or Quinlan's C4.5).
Your goal is to
convince the reader that, for each data set, either one of the two
algorithms is superior to the other (and give a significance level
as well) or that there is no statistically significant difference
between them. To accomplish this task, you may use any tools from the lecture
that you wish, under two conditions: (1) you must use the tools
correctly and thoroughly corroborate your assertion, and (2) you must
have at least one confidence interval or statistical test and at least one ROC curve in
You are to submit a detailed, well-written
with conclusions that you can justify with your results. In particular, you should
answer the following questions for both your new classifier and ID3. Did
training error go to 0? Did overfitting occur? Should you have stopped
training early? Was there a statistically significant difference between
the performance of ID3 and that of the ANN? What algorithm would you
recommend for your data sets? Of course, this is merely the minimum
that is required in your report.
Extra credit opportunities include (but are not limited to) running
on extra data sets, using other activation functions, using
multiclass data, and running
experiments on more ANN architectures and/or with more
learning rates. As always, the amount of extra credit is commensurate
with the level of extra effort and the quality of your report of the
problem is only for students registered for CSCE 878. CSCE 478 students who
do it will receive extra credit, but the amount will be less
than the number of points indicated.
- (20 pts) Consider
K : X × X
that takes two vectors from X and returns a real number.
From class we know that since K is a kernel, it computes
a dot product in an induced feature space Φ. Specifically,
Define the squared Euclidean distance between
in terms of
CSCE 478/878 (Fall 2014) Home Page
Last modified 17 October 2014; please report problems to
sscott AT cse.