Given svm objective function what will happen use large c c=100
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a) The objective function of support vector machine (SVM) can be formulated as a hinge error function with L2 regularization term, which is given by ∑_{n=1}^N E_{SV}[y_n t_n] + λ||w||^2, where E_{SV}[y_n t_n] = [1 - y_n t_n]_+ denotes the hinge error function and y_n and t_n are the prediction and the corresponding target value of the n-th sample, respectively (See Sect. 7.1.2 of Bishop's PRML). Here, we shall replace the hinge error function with the squared error function E_{SQ}[y_n t_n] = (1 - y_n t_n)^2. Explain disadvantages induced by this procedure in about 50 words. b) SVM is fundamentally a two-class classifier. In practice, we have to address problems involving K > 2 classes. Explain a procedure to build a K-class classifier by fusing multiple two-class SVMs in about 50 words.
Sri K.
Which of the following is true of the value of k in the k-Nearest Neighbors algorithm? A) The value of k is always taken as a constant and is equal to 1. If the value of k is large, it drastically increases variability. If the value of k is very large, it introduces biases into the classification decisions. If the value of k is large, the classification of a record is very sensitive to the classification of the single record to which it is closest.
Hubert A.
Here is the optimization problem for the soft-margin SVM: Minimize ||w||^2 + C * Σξ Subject to y((w^T * x) + b) ≥ 1 - ξ ξ ≥ 0 where w is the weight vector, x is the input vector, b is the bias term, y is the class label, and C is a constant. How many slack variables are there? The answer should be a function of n and d.
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