Communications of the ACM
Pattern recognition: human and mechanical
Pattern recognition: human and mechanical
A Method for Attribute Selection in Inductive Learning Systems
IEEE Transactions on Pattern Analysis and Machine Intelligence
Learnability and the Vapnik-Chervonenkis dimension
Journal of the ACM (JACM)
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Inductive Inference: Theory and Methods
ACM Computing Surveys (CSUR)
Statistical Foundations of Audit Trail Analysis for the Detection of Computer Misuse
IEEE Transactions on Software Engineering
Learning Conjunctive Concepts in Structural Domains
Machine Learning
A statistically based system for prioritizing information exploration under uncertainty
IEEE Transactions on Systems, Man, and Cybernetics, Part A: Systems and Humans
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We consider the problem of prioritizing a collection of discrete piecesof information, or transactions. The goal is to rank the transactionsin such a way that the user can best pursue a subset of the transactionsin hopes of discovering those which were generated by an interestingsource. The problem is shown to differ from traditional classification inseveral fundamental ways. Ranking algorithms are divided into classes,depending on the amount of information they may utilize. We demonstratethat while ranking by the least constrained algorithm class is consistentwith classification, such is not the case for a more constrainedclass of algorithms. We demonstrate also that while optimal ranking bythe former class is “easy”, optimal ranking by the latter class is NP-hard.Finally, we present detectors which solve optimally restricted versionsof the ranking problem, including symmetric anomaly detection.