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CLEAR
2007
Springer
179views Biometrics» more  CLEAR 2007»
15 years 6 months ago
HMM-Based Acoustic Event Detection with AdaBoost Feature Selection
Given the spectral difference between speech and acoustic events, we propose using Kullback-Leibler distance to quantify the discriminant capability of all speech feature componen...
Xi Zhou, Xiaodan Zhuang, Ming Liu, Hao Tang, Mark ...
IEEEARES
2008
IEEE
15 years 6 months ago
A Preliminary Investigation of Skype Traffic Classification Using a Minimalist Feature Set
In this work, AdaBoost and C4.5, are employed for classifying Skype direct (UDP and TCP) communications from traffic log files. Pre-processing is applied to the traffic data to ex...
Duffy Angevine, A. Nur Zincir-Heywood
113
Voted
ICMLA
2008
15 years 1 months ago
Predicting Algorithm Accuracy with a Small Set of Effective Meta-Features
We revisit 26 meta-features typically used in the context of meta-learning for model selection. Using visual analysis and computational complexity considerations, we find 4 meta-f...
Jun Won Lee, Christophe G. Giraud-Carrier
116
Voted
CVPR
2005
IEEE
16 years 2 months ago
Online Detection and Classification of Moving Objects Using Progressively Improving Detectors
Boosting based detection methods have successfully been used for robust detection of faces and pedestrians. However, a very large amount of labeled examples are required for train...
Omar Javed, Saad Ali, Mubarak Shah
NIPS
2008
15 years 1 months ago
MCBoost: Multiple Classifier Boosting for Perceptual Co-clustering of Images and Visual Features
We present a new co-clustering problem of images and visual features. The problem involves a set of non-object images in addition to a set of object images and features to be co-c...
Tae-Kyun Kim, Roberto Cipolla