By Bill J Mitchell
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The algorithm removes any examples from the class that satisfy the rule and continues until no examples remain. It repeats with each class in the set of examples. AQ19  is an implementation of this speciﬁc-to-general 3 Some Basic Concepts of Machine Learning and Data Mining 31 algorithm, while CN2  is similar, but implements a general-to-speciﬁc algorithm. RIPPER  also forms rules, but by growing them by repeatedly adding conditions. Once formed, RIPPER prunes the rules to remove ineffective sequences of conditions.
It was an attempt to introduce some of the topics discussed in the research contributions, and as such, it – hopefully – will give readers new to these techniques a better ability to read the research contributions of this volume. If not, then perhaps it has provided references for further study. Part II Research Contributions 4 Learning to Detect Malicious Executables Jeremy Z. Kolter and Marcus A. 1 Introduction Malicious code is “any code added, changed, or removed from a software system to intentionally cause harm or subvert the system’s intended function” [95, p.
In most cases, the model generalizes the examples, which is a desirable property, for we need not collect all possible examples to produce a useful model. Moreover, we can use the model to make predictions about examples absent from the original set. Researchers have used a variety of representations for such models, including trees, rules, graphs, probabilities, ﬁrst-order logic, the examples themselves, and coeﬃcients of linear and nonlinear equations. Once we have formed a set of examples, then learning and mining algorithms can support a variety of analysis tasks.