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» Using Active Learning in Intrusion Detection
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AIME
1997
Springer
15 years 2 months ago
Detecting Very Early Stages of Dementia from Normal Aging with Machine Learning Methods
We used Machine Learning (ML) methods to learn the best decision rules to distinguish normal brain aging from the earliest stages of dementia using subsamples of 198 normal and 244...
William Rodman Shankle, Subramani Mani, Michael J....
PAMI
2006
192views more  PAMI 2006»
14 years 10 months ago
Building Models of Animals from Video
This paper argues that tracking, object detection, and model-building are all similar activities. We describe a fully automatic system that builds 2D articulated models known as pi...
Deva Ramanan, David A. Forsyth, Kobus Barnard
AHSWN
2010
177views more  AHSWN 2010»
14 years 10 months ago
Dynamic Point Coverage Problem in Wireless Sensor Networks: A Cellular Learning Automata Approach
One way to prolong the lifetime of a wireless sensor network is to schedule the active times of sensor nodes, so that a node is active only when it is really needed. In the dynami...
Mehdi Esnaashari, Mohammad Reza Meybodi
IJCAI
2007
15 years 1 days ago
Common Sense Based Joint Training of Human Activity Recognizers
Given sensors to detect object use, commonsense priors of object usage in activities can reduce the need for labeled data in learning activity models. It is often useful, however,...
Shiaokai Wang, William Pentney, Ana-Maria Popescu,...
TJS
2010
182views more  TJS 2010»
14 years 9 months ago
A novel unsupervised classification approach for network anomaly detection by k-Means clustering and ID3 decision tree learning
This paper presents a novel host-based combinatorial method based on k-Means clustering and ID3 decision tree learning algorithms for unsupervised classification of anomalous and ...
Yasser Yasami, Saadat Pour Mozaffari