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» Hidden Markov Models with Multiple Observation Processes
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WISEC
2010
ACM
15 years 11 months ago
pBMDS: a behavior-based malware detection system for cellphone devices
Computing environments on cellphones, especially smartphones, are becoming more open and general-purpose, thus they also become attractive targets of malware. Cellphone malware no...
Liang Xie, Xinwen Zhang, Jean-Pierre Seifert, Senc...
130
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BMCBI
2004
111views more  BMCBI 2004»
15 years 4 months ago
The Hotdog fold: wrapping up a superfamily of thioesterases and dehydratases
Background: The Hotdog fold was initially identified in the structure of Escherichia coli FabA and subsequently in 4-hydroxybenzoyl-CoA thioesterase from Pseudomonas sp. strain CB...
Shane C. Dillon, Alex Bateman
ECML
2006
Springer
15 years 7 months ago
Unsupervised Multiple-Instance Learning for Functional Profiling of Genomic Data
Multiple-instance learning (MIL) is a popular concept among the AI community to support supervised learning applications in situations where only incomplete knowledge is available....
Corneliu Henegar, Karine Clément, Jean-Dani...
PIMRC
2008
IEEE
15 years 10 months ago
The Wireless Engset Multi-Rate Loss Model for the Handoff traffic analysis in W-CDMA networks
—The call-level performance modelling and evaluation of 3G cellular networks is important for the proper network dimensioning and efficient use of the network resources, such as ...
Vassilios G. Vassilakis, Michael D. Logothetis
ICML
2010
IEEE
15 years 2 months ago
Constructing States for Reinforcement Learning
POMDPs are the models of choice for reinforcement learning (RL) tasks where the environment cannot be observed directly. In many applications we need to learn the POMDP structure ...
M. M. Hassan Mahmud