The Experts below are selected from a list of 20697 Experts worldwide ranked by ideXlab platform
Deborah Silver - One of the best experts on this subject based on the ideXlab platform.
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iconic techniques for Feature Visualization
IEEE Visualization, 1995Co-Authors: Frits H Post, Frank J Post, Theo Van Walsum, Deborah SilverAbstract:This paper presents a conceptual framework and a process model for Feature extraction and iconic Visualization. Feature extraction is viewed as a process of data abstraction, which can proceed in multiple stages and corresponding data abstraction levels. The Features are represented by attribute sets, which play a key role in the Visualization process. Icons are symbolic parametric objects, designed as visual representations of Features. The attributes are mapped to the parameters (or degrees of freedom) of an icon. We describe some generic techniques to generate attribute sets, such as volume integrals and medial axis transforms. A simple but powerful modeling language was developed to create icons, and to link the attributes to the icon parameters. We present illustrative examples of iconic Visualization created with the techniques described, showing the effectiveness of this approach.
Margeret Hall - One of the best experts on this subject based on the ideXlab platform.
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Visualization Feature selection machine learning identifying the responsible group for extreme acts of violence
IEEE Access, 2018Co-Authors: Mahdi Hashemi, Margeret HallAbstract:The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 Features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the Visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward Feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected Features of a violent act. Top-ranked selected Features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy.
Yu Liu - One of the best experts on this subject based on the ideXlab platform.
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modeco an integrated software package for ecological niche modeling
Ecography, 2010Co-Authors: Qinghua Guo, Yu LiuAbstract:ModEco is a software package for ecological niche modeling. It integrates a range of niche modeling methods within a geographical information system. ModEco provides a user friendly platform that enables users to explore, analyze, and model species distribution data with relative ease. ModEco has several unique Features: 1) it deals with different types of ecological observation data, such as presence and absence data, presence-only data, and abundance data; 2) it provides a range of models when dealing with presence-only data, such as presence-only models, pseudo-absence models, background vs presence data models, and ensemble models; and 3) it includes relatively comprehensive tools for data Visualization, Feature selection, and accuracy assessment.
Frits H Post - One of the best experts on this subject based on the ideXlab platform.
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iconic techniques for Feature Visualization
IEEE Visualization, 1995Co-Authors: Frits H Post, Frank J Post, Theo Van Walsum, Deborah SilverAbstract:This paper presents a conceptual framework and a process model for Feature extraction and iconic Visualization. Feature extraction is viewed as a process of data abstraction, which can proceed in multiple stages and corresponding data abstraction levels. The Features are represented by attribute sets, which play a key role in the Visualization process. Icons are symbolic parametric objects, designed as visual representations of Features. The attributes are mapped to the parameters (or degrees of freedom) of an icon. We describe some generic techniques to generate attribute sets, such as volume integrals and medial axis transforms. A simple but powerful modeling language was developed to create icons, and to link the attributes to the icon parameters. We present illustrative examples of iconic Visualization created with the techniques described, showing the effectiveness of this approach.
Mahdi Hashemi - One of the best experts on this subject based on the ideXlab platform.
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Visualization Feature selection machine learning identifying the responsible group for extreme acts of violence
IEEE Access, 2018Co-Authors: Mahdi Hashemi, Margeret HallAbstract:The toll of human casualties and psychological impacts on societies make any study on violent extremism worthwhile, let alone attempting to detect patterns among them. This paper is an effort to predict which violent extremist organization (VEO), among 14 currently active ones throughout the world, is responsible for a violent act based on 14 Features, including its human and structural tolls, its target type and value, intelligence, and weapons utilized in the attack. Three main steps in our paper include: 1) the Visualization of the violent acts through linear and non-linear dimensionality reduction techniques; 2) sequential forward Feature selection based on the generalization accuracy of three machine learning models–decision tree, and linear and nonlinear SVM; and 3) employing multilayer perceptron to predict the VEO based on the selected Features of a violent act. Top-ranked selected Features were related to the target type and plan and the multilayer perceptron achieved up to 40% test accuracy.
