Machine Learning confronted with the operational constraints of detection systems

Machine learning with operational costs

This work proposes a way to align statistical modeling with decision making. We provide a method that propagates the uncertainty in predictive modeling to the uncertainty in operational cost, where operational cost is the amount spent by the practitioner in solving the problem. The method allows us to explore the range of operational costs associated with the set of reasonable statistical model...

Distributed Machine Learning with Communication Constraints

Distributed Machine Learning with Communication Constraints by Yuchen Zhang Doctor of Philosophy in Computer Science University of California, Berkeley Professor Michael I. Jordan, Co-chair Professor Martin J. Wainwright, Co-chair Distributed machine learning bridges the traditional fields of distributed systems and machine learning, nurturing a rich family of research problems. Classical machi...

Modeling Intrusion Detection Systems With Machine Learning And Selected Attributes

Intrusion Detection System (IDS) has become a vital component in ensuring the safety of today’s network. In this review, we does experimentation on NSL KDD Cup -20% dataset with selected attribute to improve the performance. The time complexity is improved by reducing false alarm rate with Machine Learning (ML) algorithms. This improve the detection rate for DoS, Probe, U2R and R2L attacks. In ...

the effects of integrating cooperative learning into vocabulary learning of elementary school students

the purpose of the research is to examine if integrating cooperative learning into vocabulary learning helps to increase word recognition of students in an elementary school in iran. it tries to investigate whether cooperative learning approach enables students to improve their language learning. this research used stad (students team achievement division) as a cooperative model in this study. ...

Anomaly detection with Machine learning