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• W802533246 abstract "The challenge of target classification is addressed in this work with both feature extraction and classifier hyperparameter optimization investigations. Simu­ lated and measured high-range resolution radar data is processed, features are se­ lected, and the resulting features are given to a classifier. For feature extraction, we examine two techniques. The first is a supervised method requiring an expert to identify and construct features. The performance of this approach served as moti­ vation for the second technique, an automated wavelet packet basis approach. For this approach, we develop the Kolmogorov-Smimov best-basis technique that util­ izes empirical cumulative distribution functions and results in improved classifica­ tion performance at low dimensionality. To measure classification efficacy, we use a quadratic Bayesian classifier, which assumes a Gaussian distribution as well as a support vector machine. The support vector machine is a classifier, which has gen­ erated excitement and interest in the pattern recognition community due to its gen­ eralization, performance, and ability to operate in high dimensional feature spaces. Although support vector machines are generated without the use of user-specified models, required hyperparameters, such as kernel width, are usually user-specified or experimentally derived. We develop techniques to optimize selection of these hyperparameters. These approaches allow us to characterize the problem, ulti­ mately resulting in an automated approach for optimization, semi-alignment." @default.
• W802533246 created "2016-06-24" @default.
• W802533246 creator A5032726597 @default.
• W802533246 date "2004-01-01" @default.
• W802533246 modified "2023-09-27" @default.
• W802533246 title "EXPLOITING HIGH DIMENSIONAL DATA FOR SIGNAL CHARACTERIZATION AND CLASSIFICATION IN FEATURE SPACE" @default.
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• W802533246 hasPublicationYear "2004" @default.
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