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BIBE
2007
IEEE
162views Bioinformatics» more  BIBE 2007»
16 years 26 days ago
An Investigation into the Feasibility of Detecting Microscopic Disease Using Machine Learning
— The prognosis for many cancers could be improved dramatically if they could be detected while still at the microscopic disease stage. We are investigating the possibility of de...
Mary Qu Yang, Jack Y. Yang
STOC
1993
ACM
141views Algorithms» more  STOC 1993»
15 years 10 months ago
Bounds for the computational power and learning complexity of analog neural nets
Abstract. It is shown that high-order feedforward neural nets of constant depth with piecewisepolynomial activation functions and arbitrary real weights can be simulated for Boolea...
Wolfgang Maass
BMCBI
2010
229views more  BMCBI 2010»
15 years 6 months ago
Mocapy++ - A toolkit for inference and learning in dynamic Bayesian networks
Background: Mocapy++ is a toolkit for parameter learning and inference in dynamic Bayesian networks (DBNs). It supports a wide range of DBN architectures and probability distribut...
Martin Paluszewski, Thomas Hamelryck
IDEAL
2003
Springer
15 years 11 months ago
Detecting Distributed Denial of Service (DDoS) Attacks through Inductive Learning
As the complexity of Internet is scaled up, it is likely for the Internet resources to be exposed to Distributed Denial of Service (DDoS) flooding attacks on TCP-based Web servers....
Sanguk Noh, Cheolho Lee, Kyunghee Choi, Gihyun Jun...
IJCNN
2000
IEEE
15 years 11 months ago
Regression Analysis for Rival Penalized Competitive Learning Binary Tree
The main aim of this paper is to develop a suitable regression analysis model for describing the relationship between the index efficiency and the parameters of the Rival Penaliz...
Xuequn Li, Irwin King