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Q5. According to the method obtained in Q2, draw a block diagram at SVM level to show the structure of the multi-class classifier constructed by linear SVMs. Explain the design (e.g., number of inputs, number of outputs, number f SVMs used, class label assignment, etc.) and describe how this multi-class classifier works. Remark: A blocking diagram is a diagram which is used to, say, show a concept or a structure, etc. Here in this question, a diagram is used to show the structure of the multi-class SVM classifier, i.e., how to put binary SVM classifiers together to work as a multi-class SVM classifier. For example, Q5 of tutorial 8 is an example of a block diagram at SVM level. Neural network diagram is a kind of diagram to show its structure at neuron level. The block diagrams in lecture 9 are to show the architecture of ensemble classifier, etc. (20 Marks) Q6. According to your dataset in Q3 and the design of your multi-class classifier in Q5, identify the support vectors of the linear SVMs by "inspection" and design their hyperplanes by hand. Show the calculations and explain the details of your design. (20 Marks) Q7. Produce a test dataset by averaging the samples for each row in Table 2, i.e., (sam- ple of class 1 + sample of class 2 + sample of class 3)/3. Summarise the results in the form of Table 3, where N is the number of SVMs in your design and "Classi- fication" is the class determined by your multi-class classifier. Explain how to get the "Classification" column using one test sample. Show the calculations for one or two samples to demonstrate how to get the contents in the table. (20 Marks) Test Sample Output of SVM 1 Output of SVM N Classification Table 3: Summary of classification accuracy. Marking: The learning outcomes of this assignment are that student understands the fundamental principle and theory of support vector machine (SVM) classifier; is able to design multi-class SVM classifier for linearly separable dataset and knows how to de- termine the classification of test samples with the designed classifier. The assessment will look into the knowledge and understanding on the topic. When answering the ques- tions, show/explain/describe clearly the steps/design/concepts with reference to the equa- tions/theory/algorithms (stated in the lecture slides). When making comments (if neces- sary), provide statements with the support from the results obtained.

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