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Full-Text Articles in Computer Sciences
Gender And Ethnicity Classification Using Partial Face In Biometric Applications, Jamie Lyle
Gender And Ethnicity Classification Using Partial Face In Biometric Applications, Jamie Lyle
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As the number of biometric applications increases, the use of non-ideal information such as images which are not strictly controlled, images taken covertly, or images where the main interest is partially occluded, also increases. Face images are a specific example of this. In these non-ideal instances, other information, such as gender and ethnicity, can be determined to narrow the search space and/or improve the recognition results. Some research exists for gender classification using partial-face images, but there is little research involving ethnic classifications on such images. Few datasets have had the ethnic diversity needed and sufficient subjects for each ethnicity …
Fast And Efficient Classification, Tracking, And Simulation In Wireless Sensor Networks, Hao Jiang
Fast And Efficient Classification, Tracking, And Simulation In Wireless Sensor Networks, Hao Jiang
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Wireless sensor networks are composed of large numbers of resource-lean sensors that collect low-level inputs from the physical world. The applications present challenges for programmers. On the one hand, lightweight algorithms are required given the limited capacity of the constituent devices. On the other, the algorithms must be scalable to accommodate large networks. In this thesis, we focus on the design and implementation of fast and lean (yet scalable) algorithms for classification, simulation, and target tracking in the context of wireless sensor networks. We briefly consider each of these challenges in turn.
The first challenge is to achieve high precision …
Evolutionary Strategies For Data Mining, Rose Lowe
Evolutionary Strategies For Data Mining, Rose Lowe
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Learning classifier systems (LCS) have been successful in generating rules for solving classification problems in data mining. The rules are of the form IF condition THEN action. The condition encodes the features of the input space and the action encodes the class label. What is lacking in those systems is the ability to express each feature using a function that is appropriate for that feature. The genetic algorithm is capable of doing this but cannot because only one type of membership function
is provided. Thus, the genetic algorithm learns only the shape and placement of the membership function, and in …