Department of Informatics, King’s College London Pattern Recognition (6CCS3PRE/7CCSMPNN).
Assignment: Support Vector Machines (SVMs) and Ensemble Methods
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 (before Q8) a classification problem of 3 classes. A multi-class SVM-based classifier formed by multiple SVMs is designed to deal with the classification problem. And after Q8 (included Q8) considers your “own created” dataset to investigate the classification performance using the techniques of Bagging and Boosting. Some simple “weak” classifiers will be designed and combined to achieve an improved classification performance for a two-class classification problem.
Q1. Write down your 7-digit student ID denoted as s1s2s3s4s5s6s7. (5 Marks)
Q2. Find R1 which is the remainder of . Table 1 shows the multi-class methods to be used corresponding to the value of R1 obtained. (5 Marks)
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)