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Worms are self-contained programs that spread over the Internet. Worms cause problems such as lost of information, information theft and denial-of-service attacks. The first part of the paper evaluates the detection of worms based on content classification by using all machine learning techniques available in WEKA data mining tools. Four most accurate and quite fast classifiers are identified for...
Context extraction for local fusion (CELF) is a local approach that combines multiple classifier outputs with the help of feature space information. CELF is based on an objective function that integrates context extraction and decision fusion. Context extraction divides the feature space into homogeneous regions; decision fusion combines multiple classifier outputs in each region or context. Although...
Feature subset selection is a well studied problem in machine learning. One short-coming of many methods is the selection of highly correlated features; a characteristic of hyperspec-tral data. A novel stochastic feature selection method with three major components is presented. First, we present an optimized feature selection method that maximizes a heuristic using a simulated annealing search which...
When tracking people or other moving objects with a mobile robot, detection is the first and most critical step. At first most researchers focused on the tracking algorithms, but recently AdaBoost (supervised machine learning technique) was used for people legs detection in 2D range data. The results are promising, but it is unclear if the obtained classifier could be used on the data from another...
The classification for the noisy training data in high dimension suffers from concurrent negative effects by noise and irrelevant/redundant features. Noise disrupts the training data and irrelevant/redundant features prevent the classifier from picking relevant features in building the model. Therefore they may reduce classification accuracy. This paper introduces a novel approach to improve the quality...
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