SVM (Support vector machine) is a powerful classifier. We can use SVM to find a hyperplane to devide the sample space into two parts.
Naive Bayes is a classigier based on a strong assumption that all the features of a document are independent.
Given $N$ independent and identically distributed (i.i.d) variables $X_1, X_2 …, X_N$ from a distribution $D$ with a parameter $\theta$, we can use a function $f$ to estimate $\theta$.
In Linux 0.11, the all kinds of reading/writing operations are done with the help of buffer. Every changes is reflected on the buffer and then synchronize with devices.