1 INTRODUCTION  
2 DATA  
3 TESTING AND PERFORMANCE MEASURES  
3.1 Testing  
3.2 Performance Measures  
4 FEATURE EXTRACTION  
4.1 Principal Component Analysis  
4.2 LDA  
4.3 Feature Sets  
5 MACHINE LEARNING TECHNIQUES  
5.1 Minimum Error Classification  
5.2 Naïve Bayes  
5.3 K-Nearest Neighbours  
5.4 Support Vector Machine  
5.5 Artificial Neural Network  
5.6 Random Forest  
6 RESULTS  
6.1 Best Performing Classifier  
6.2 Inter-Classifier Agreement  
6.3 Classifier Performance on Different Classes of Images  
6.4 Examples of Incorrectly Classified Objects  
6.5 Performance on Spectroscopically Confirmed SNe  
6.6 Comparison with Human Scanners  
7 CONCLUSIONS AND FUTURE WORK