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Perceptron, SGD, Boosting

1. Consider running the Perceptron algorithm on a training set S arranged in a certain order. Now suppose we run it with the same initial weights and on the same training set but in a different order, S′. Does Perceptron make the same number of mistakes? Does it end up with the same final weights? If so, prove it. If not, give a counterexample, i.e. an S and S′ where order matters.

2. We have mainly focused on squared loss, but there are other interesting losses in machine learning. Consider the following loss function which we denote by φ(z) = max(0,−z). Let S be a training set (x1,y1), . . . , (xm,ym) where each xi ∈ Rn and yi ∈{−1, 1}. Consider running stochastic gradient descent (SGD) to find a weight vector w that minimizes 1

m

∑m i=1 φ(y

i · wTxi). Explain the explicit relationship between this algorithm and the Perceptron algorithm. Recall that for SGD, the update rule when the ith example is picked at random is

wnew = wold −η∇φ ( yiwTxi

) .

3. Here we will give an illustrative example of a weak learner for a simple concept class. Let the

domain be the real line, R, and let C refer to the concept class of “3-piece classifiers”, which are functions of the following form: for θ1 < θ2 and b ∈ {−1, 1}, hθ1,θ2,b(x) is b if x ∈ [θ1,θ2] and −b otherwise. In other words, they take a certain Boolean value inside a certain interval and the opposite value everywhere else. For example, h10,20,1(x) would be +1 on [10, 20], and −1 everywhere else. Let H refer to the simpler class of “decision stumps”, i.e. functions hθ,b such that h(x) is b for all x ≤ θ and −b otherwise.

(a) Show formally that for any distribution on R (assume finite support, for simplicity; i.e., assume the distribution is bounded within [−B, B] for some large B) and any unknown labeling function c ∈ C that is a 3-piece classifier, there exists a decision stump h ∈ H that has error at most 1/3, i.e. P[h(x) 6= c(x)] ≤ 1/3.

(b) Describe a simple, efficient procedure for finding a decision stump that minimizes error

with respect to a finite training set of size m. Such a procedure is called an empirical risk minimizer (ERM).

(c) Give a short intuitive explanation for why we should expect that we can easily pick m

sufficiently large that the training error is a good approximation of the true error, i.e. why we can ensure generalization. (Your answer should relate to what we have gained in going from requiring a learner for C to requiring a learner for H.) This lets us conclude that we can weakly learn C using H.

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4. Consider an iteration of the AdaBoost algorithm (using notation from the video lecture on Boosting) where we have obtained classifer ht. Show that with respect to the distribution Dt+1 generated for the next iteration, ht has accuracy exactly 1/2.

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