Zobrazeno 1 - 10
of 118
pro vyhledávání: '"Marcus A. Maloof"'
Publikováno v:
IEEE Security & Privacy Magazine. 7:14-21
MITRE researchers designed a prototype system for identifying insider threats, which prompted a team of engineers and social scientists to experimentally study how malicious insiders use information differently from a benign baseline group. This rese
Publikováno v:
Financial Cryptography and Data Security ISBN: 9783662478530
Financial Cryptography
Financial Cryptography
In a re-identification attack, an adversary analyzes the sizes of intercepted encrypted VoIP packets to infer characteristics of the underlying audio—for example, the language or individual phrases spoken on the encrypted VoIP call. Traffic morphin
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3c2efccbbabd0c86ff9d44f790abe2e8
https://doi.org/10.1007/978-3-662-47854-7_5
https://doi.org/10.1007/978-3-662-47854-7_5
Autor:
Marcus A. Maloof, Ryszard S. Michalski
Publikováno v:
Artificial Intelligence. 154(1-2):95-126
Agents that learn on-line with partial instance memory reserve some of the previously encountered examples for use in future training episodes. In earlier work, we selected extreme examples—those from the boundaries of induced concept descriptions
Publikováno v:
IEEE Transactions on Pattern Analysis and Machine Intelligence. 25:1561-1569
The conventional wisdom in the field of statistical pattern recognition (SPR) is that the size of the finite test sample dominates the variance in the assessment of the performance of a classical or neural classifier. The present work shows that this
Autor:
Marcus A. Maloof, Ryszard S. Michalski
Publikováno v:
Machine Learning. 41:27-52
This paper describes a method for selecting training examples for a partial memory learning system. The method selects extreme examples that lie at the boundaries of concept descriptions and uses these examples with new training examples to induce ne
Autor:
Ryszard S. Michalski, Marcus A. Maloof
Publikováno v:
Expert Systems with Applications. 12:11-20
In this paper we describe a method for learning shape descriptions of objects in X-ray images. The descriptions are induced from shape examples using the AQ15c inductive learning system. The method has been experimentally compared to k-nearest neighb
Publikováno v:
Proceedings of the 22nd ACM international conference on Conference on information & knowledge management - CIKM '13.
Publikováno v:
CIKM
Scaling high-quality, cost-efficient query evaluation is critical to search system performance. Although partial indexes reduce query processing times, result quality may be jeopardized due to exclusion of relevant non-local documents. Selectively fo
Autor:
Marcus A. Maloof
Publikováno v:
Advances in Machine Learning I ISBN: 9783642051760
Advances in Machine Learning I
Advances in Machine Learning I
Since the mid-1990’s, we have developed, implemented, and evaluated a number of learning methods that cope with concept drift. Drift occurs when the target concept that a learner must acquire changes over time. It is present in applications involvi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::52b4a13d6d8439087eb3d7836189614b
https://doi.org/10.1007/978-3-642-05177-7_2
https://doi.org/10.1007/978-3-642-05177-7_2
Autor:
Marcus A. Maloof
Publikováno v:
Machine Learning in Cyber Trust ISBN: 9780387887340
We present results from an empirical study of seven online-learning methods on the task of detecting previously unseen malicious executables. Mali- cious software has disrupted computer and network operation and has compro- mised or destroyed sensiti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::d7ab144a98c624d5c294364f39dc1bc8
https://doi.org/10.1007/978-0-387-88735-7_5
https://doi.org/10.1007/978-0-387-88735-7_5