Separation results for boolean function classes

Learnability and Characterization Results for Classes of Boolean Functions

On PAC Learning Algorithms for Rich Boolean Function Classes

We survey the fastest known algorithms for learning various expressive classes of Boolean functions in the Probably Approximately Correct (PAC) learning model.

Further Results on Fault Classes in Boolean Specifications

Fault conditions can be used to analyze existing testing methods. The hierarchy of fault classes may not only explain experimental results of various testing methods, but also help to design effective test cases. We extend the known hierarchy to a wider range of fault classes based on detection conditions and prove the relationships between them. As a result, we show that for fault classes disc...

Six Degrees of Separation in Boolean Function Networks with Neutrality

Abstract. We analyze two Boolean function networks with different degrees of neutrality. The results show that the one with explicit neutrality is a small-world network where each pair of possible solutions has a short distance and most of the possible solutions are highly clustered. These network structural properties owe their existence to the “short cuts” introduced by redundant genes in the...

Some Separation Results Between Classes of Pointer Algorithms

We investigate the relative computational power of three classes of pointer algorithms by comparing time and space usage. These classes include two from Tarjan’s original paper and another class which we introduce which is related to another long standing problem.