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» Probabilistic Neural Network Models for Sequential Data
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PAKDD
2000
ACM
161views Data Mining» more  PAKDD 2000»
13 years 11 months ago
Adaptive Boosting for Spatial Functions with Unstable Driving Attributes
Combining multiple global models (e.g. back-propagation based neural networks) is an effective technique for improving classification accuracy by reducing a variance through manipu...
Aleksandar Lazarevic, Tim Fiez, Zoran Obradovic
NN
2010
Springer
225views Neural Networks» more  NN 2010»
13 years 6 months ago
Learning to imitate stochastic time series in a compositional way by chaos
This study shows that a mixture of RNN experts model can acquire the ability to generate sequences that are combination of multiple primitive patterns by means of self-organizing ...
Jun Namikawa, Jun Tani
SSDBM
1998
IEEE
115views Database» more  SSDBM 1998»
14 years 8 days ago
Tools for Data Warehouse Quality
In this demonstration, we show three interrelated tools intended to improve different aspects of the quality of data warehouse solutions. Firstly, the deductive object manager Con...
Michael Gebhardt, Matthias Jarke, Manfred A. Jeusf...
CORR
2010
Springer
80views Education» more  CORR 2010»
13 years 8 months ago
Multi-path Probabilistic Available Bandwidth Estimation through Bayesian Active Learning
Knowing the largest rate at which data can be sent on an end-to-end path such that the egress rate is equal to the ingress rate with high probability can be very practical when ch...
Frederic Thouin, Mark Coates, Michael Rabbat
RAID
2004
Springer
14 years 1 months ago
Anomaly Detection Using Layered Networks Based on Eigen Co-occurrence Matrix
Anomaly detection is a promising approach to detecting intruders masquerading as valid users (called masqueraders). It creates a user profile and labels any behavior that deviates...
Mizuki Oka, Yoshihiro Oyama, Hirotake Abe, Kazuhik...