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Problem set 4 for the cs446: pattern recognition and machine learning course, which was handed out on october 9, 2008, and was due on october 20, 2008. The problem set includes instructions for students, as well as three problems related to margin computation, vc dimension, and constructing kernels. The problems involve calculating margins for given datasets, determining linear threshold functions and minimum distances for different concepts, and understanding the vc dimension and constructing kernels.
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CS446: Pattern Recognition and Machine Learning Fall 2008
Handed Out: October 9, 2008 Due: October 20, 2008
γ = mini
∣w· ‖xwi−‖θ
(a) In the file hw4 1a.dat we have given you set of 50 points in { 0 , 1 }^20 : 20 dimen- sional binary vectors. Using the hyperplane w ∈ <^20 :
w =< − 1 , 1 , 1 , 0 , 1 , 0 , 0 , 0 , 0 , 0 , − 1 , 1 , − 1 , 0 , 1 , 1 , 0 , 0 , 0 , − 1 >, θ = 1. 5
Classify each of the points in hw4 1a.dat and compute the margin of this data set with respect to this hyperplane. (No code is needed for any of these problems though. Do the calculations however you please.) [5 points] (b) Now using the sparse disjunction x 3 ∨ x 6 ∨ x 9 ∨ x 12 : i. Come up with a linear threshold function that represents this concept. Using this hyperplane, label hw4 1a.dat and compute the margin. [5 points] ii. What is the minimum distance between a positive and negative example. [ points] (c) Now using the denser disjunction x 2 ∨ x 4 ∨ x 6 ∨ x 8 ∨ x 10 ∨ x 12 ∨ x 14 ∨ x 16 ∨ x 18 : i. Come up with a linear threshold function that represents this concept. Using this hyperplane, label hw4 1a.dat and compute the margin. [5 points] ii. What is the minimum distance between a positive and negative example. [ points]
(d) What may explain the differences in minimum distances between positive and negative examples in the two concepts above? What do the margins and separat- ing distance in part (c) and (d) tell you about the difficulty of learning a sparse vs. dense disjunction? [10 points]
(a) Consider the following learning task. Examples are points (x, y) ∈ <^2. The concept space H is the collection of all hinged lines. That is, each concept h ∈ H is formed by two lines that intersect at a point and divide the plane based on their intersection. We can define this space by a, b, c, d, e, f ∈ < such that example (x, y) is labeled positive if and only if (a ∗ x + b ∗ y > c and d ∗ x + e ∗ y > f ), and the first line segment (a ∗ x + b ∗ y = c) is not parallel to the second line segment (d ∗ x + e ∗ y = f ). See figure 1.
Give the VC dimension of H, and justify your answer. [10 points] (b) Now assume that the concept space H′^ is the collection of all 2-hinge lines. That is, each concept h′^ ∈ H′^ is formed by a boundary of three line segments with two hinges, defined by a, b, c, d, e, f, g, h, i ∈ < such that example (x, y) is labeled positive if and only if (a ∗ x + b ∗ y > c and d ∗ x + e ∗ y > f and g ∗ x + h ∗ y > i) and the sequential line segments are not parallel. Give the VC dimension of H′, and justify your answer. [10 points] (c) What is the VC dimension of the space of K-hinge lines, in other words the boundary is made up of K + 1 line segments? Justify your answer. How many parameters are necessary to specify a hypothesis in said K-hinge space. [15 points]