To examine classification for k-fold cross-validation and naïve Bayes, two packages contain the necessary functions for partitioning the data. These are: caret & klaR caret & Crisp klaR & SEMMA predictive & caret
Added by Renee W.
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In this technique, the data is divided into k subsets, and the model is trained on k-1 subsets and tested on the remaining subset. This process is repeated k times, and the average performance is calculated. Naïve Bayes is a classification algorithm based on Show more…
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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.
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