7CCSMPNN Support Vector Machines

Pattern Recognition, Neural Networks and Deep Learning

(7CCSMPNN).

Assignment: Support Vector Machines (SVMs)

This coursework is assessed. A type-written report needs to be submitted online

through KEATS by the deadline specified on the module’s KEATS webpage. In this

coursework, we consider a classification problem of 3 classes. A multi-class SVM-based

classifier formed by multiple SVMs is designed to deal with the classification problem.

Q1. Write down your 7-digit student ID denoted as s1s2s3s4s5s6s7. (5 Marks)

Q2. Find R1 which is the remainder of s1+s2+s3+s4+s5+s6+s7

. Table 1 shows the multi￾class methods to be used corresponding to the value of R1 obtained. (5 Marks)

R1 Method

0 One against one

1 One against all

2 Binary decision tree

3 Binary coded

Table 1: R1 and its corresponding multi-class method.

Q3. Create a linearly separable two-dimensional dataset of your own, which consists of

3 classes. List the dataset in the format as shown in Table 2. Each class should

contain at least 10 samples and all three classes have the same number of samples.

Note: This is your own created dataset. The chance of having the same dataset

in other submissions is slim. Do not share your dataset with others to avoid any

plagiarism/collusion issues. (10 Marks)

Sample of Class 1 Sample of Class 2 Sample of Class 3

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.

Purposes of Assignment: This assignment provides the overall classification idea from

samples to design to classification. It helps you to make clear the concept, working prin￾ciple, theory, classification of samples, design procedure and multiple-class classification

techniques for SVM.