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This paper introduces a new database structure which is different from the existed one that only consists of press and release time. To improve, we add eigenvalue of pressure to it and we compare the results of new database structure with the old one to clarify whether the adding eigenvalue of pressure can improve the efficiency of the system. In addition, we come up a different keystroke dynamics method, Hidden Markov Models, to examine the efficiency of the new database structure and compare it with the results from another similar one, One-Class Support Vector Machine, to figure out whether Hidden Markov Models will behave better.

This paper proposes a new definition of VC dimension based on probability. The VC dimension is the most important parameter to evaluate the learning ability of a hypothesis set. However, in many actual problems, the origin VC dimension is always too large to use. And there always exists a paradox between the size of hypothesis set and the range of confidence interval. This paper solves these problems by taking the information of data set and probability into consideration. We propose a new definition of VC dimension based on probability and gives a stronger VC bound.