Zobrazeno 1 - 7
of 7
pro vyhledávání: '"Jayaram Raghuram"'
Publikováno v:
Journal of Advanced Research, Vol 5, Iss 4, Pp 423-433 (2014)
We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), m
Externí odkaz:
https://doaj.org/article/bd073e14629e444b8c1ab4b1898e4600
Publikováno v:
IEEE Transactions on Neural Networks and Learning Systems. 25:1520-1537
A new method for semisupervised learning from pairwise sample (must- and cannot-link) constraints is introduced. It addresses an important limitation of many existing methods, whose solutions do not achieve effective propagation of the constraint inf
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::7fe7de835a14819834dabd1eb471a0c4
https://doi.org/10.1109/tnnls.2013.2294459
https://doi.org/10.1109/tnnls.2013.2294459
Publikováno v:
Journal of Advanced Research
We propose a method for detecting anomalous domain names, with focus on algorithmically generated domain names which are frequently associated with malicious activities such as fast flux service networks, particularly for bot networks (or botnets), m
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5a25d724084b36e6604a7b5386a343bf
http://europepmc.org/articles/PMC4294760
http://europepmc.org/articles/PMC4294760
Publikováno v:
Neural Computation. 24:1926-1966
We introduce new inductive, generative semisupervised mixtures with more finely grained class label generation mechanisms than in previous work. Our models combine advantages of semisupervised mixtures, which achieve label extrapolation over a compon
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fe5c3e106c57cab79150ac82b01f7369
https://doi.org/10.1162/neco_a_00284
https://doi.org/10.1162/neco_a_00284
Publikováno v:
CISS
This paper introduces a method for mixture model-based classifier domain adaptation, wherein one has adequate labeled training data for one (source) domain, very scarce labeled data for another (target) domain, and where the discrepancy between the s
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1076782567eda0e6bc54fce8ca00e178
https://doi.org/10.1109/ciss.2012.6310708
https://doi.org/10.1109/ciss.2012.6310708
Autor:
Carl D. Langefeld, Jayaram Raghuram, Li Chen, David M. Herrington, Xiguo Yuan, Richard T. Guy, David J. Miller, Yue Wang, Guoqiang Yu
Publikováno v:
BMC Genomics, Vol 12, Iss 1, p 344 (2011)
BMC Genomics
BMC Genomics
Background Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different dat
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ce8d0fedea486d8bccf4f2dcf9f1f797
https://doi.org/10.1186/1471-2164-12-344
https://doi.org/10.1186/1471-2164-12-344
Autor:
David J. Miller, Jeff Rowe, Karl N. Levitt, Yuquan Shan, George Kesidis, Anna Scaglione, Jayaram Raghuram
Publikováno v:
EURASIP Journal on Advances in Signal Processing. 2015(1)
With the onset of large numbers of energy-flexible appliances, in particular plug-in electric and hybrid-electric vehicles, a significant portion of electricity demand will be somewhat flexible and accordingly may be responsive to changes in electric
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cce80acfcb79c5ac5bccece337baf135