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» Anomaly Detection Using Visualization and Machine Learning
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ACSW
2004
14 years 11 months ago
Detecting Stress in Spoken English using Decision Trees and Support Vector Machines
This paper describes an approach to the detection of stress in spoken New Zealand English. After identifying the vowel segments of the speech signal, the approach extracts two dif...
Huayang Xie, Peter Andreae, Mengjie Zhang, Paul Wa...
ASC
2008
14 years 10 months ago
Dynamic classification for video stream using support vector machine
A dynamic classification using the support vector machine (SVM) technique is presented in this paper as a new `incremental' framework for multiple-classifying video stream da...
Mariette Awad, Yuichi Motai
EDM
2010
121views Data Mining» more  EDM 2010»
14 years 8 months ago
Identifying Students' Inquiry Planning Using Machine Learning
This research investigates the detection of student meta-cognitive planning processes in real-time using log tracing techniques. We use fine and coarse-grained data distillation, i...
Orlando Montalvo, Ryan Shaun Joazeiro de Baker, Mi...
ICASSP
2011
IEEE
14 years 1 months ago
Improving kernel-energy trade-offs for machine learning in implantable and wearable biomedical applications
Emerging biomedical sensors and stimulators offer unprecedented modalities for delivering therapy and acquiring physiological signals (e.g., deep brain stimulators). Exploiting th...
Kyong-Ho Lee, Sun-Yuan Kung, Naveen Verma
JMLR
2010
185views more  JMLR 2010»
14 years 4 months ago
HMMPayl: an application of HMM to the analysis of the HTTP Payload
Zero-days attacks are one of the most dangerous threats against computer networks. These, by definition, are attacks never seen before. Thus, defense tools based on a database of ...
Davide Ariu, Giorgio Giacinto