We wish to perform classification of Breast Cancer test samples from a source data set. The

dataset consists of $569$ samples with $30$ feature measurements per sample. Your job is train on

$10\%$ of the data to see how well it will classify the remaining $90\%$ of the data using a Support

Vector Machine approach. The data points are to be classified into 'Benign' and 'Malignant'

based on the data available.

$($a$)$ From your training set, formulate a linear SVM to perform classification using either the

PCA or FLD dimensionally reduced data. Report on your classification error and your

Type I and Type II errors for cancer detection. You cannot use the MATLAB SVM

functions to perform your training or classification; formulate the problem and use the

optimization toolbox.

$($b$)$ From your training set, formulate a non$-$linear SVM to perform classification using either

the PCA or FLD dimensionally reduced data. Report on your classification error and

your Type I and Type II errors for cancer detection. You cannot use the MATLAB SVM

functions to perform your training or classification; formulate the problem and use the

optimization toolbox.