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Fast Object Localization via Sensitivity Analysis
2019Deep Convolutional Neural Networks (CNNs) have been repeatedly shown to perform well on image classification tasks, successfully recognizing a broad array of objects when given sufficient training data. Methods for object localization, however, are still in need of substantial improvement.
Mohammad K. Ebrahimpour, David C. Noelle
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A fatigue model with local sensitivity analysis
Fatigue & Fracture of Engineering Materials & Structures, 2007ABSTRACTThe goal of this paper is two fold. First, it introduces a general parametric lifetime model for high‐cycle fatigue regime derived from physical, statistical, engineering and dimensional analysis considerations. The proposed model has two threshold parameters and three Weibull distribution parameters.
E. CASTILLO +3 more
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On the Local Sensitivity Analysis of the Inoperability Input‐Output Model
Risk Analysis, 2011Natural and man‐made disasters are currently a source of major concern for contemporary societies. In order to understand their economic impacts, the inoperability input‐output model has recently gained recognition among scholars. In a recent paper, Percoco (2006) has proposed an extension of the model to map the technologically most important sectors ...
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Hoffman's Error Bound, Local Controllability, and Sensitivity Analysis
SIAM Journal on Control and Optimization, 2000Summary: Our aim is to present sufficient conditions ensuring Hoffman's error bound for lower semicontinuous nonconvex inequality systems and to analyze its impact on the local controllability, implicit function theorem for (non-Lipschitz) multivalued mappings, generalized equations (variational inequalities), and sensitivity analysis and on other ...
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Stable locality sensitive discriminant analysis for image recognition
Neural Networks, 2014Locality Sensitive Discriminant Analysis (LSDA) is one of the prevalent discriminant approaches based on manifold learning for dimensionality reduction. However, LSDA ignores the intra-class variation that characterizes the diversity of data, resulting in unstableness of the intra-class geometrical structure representation and not good enough ...
Quanxue Gao +4 more
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A Novel Local Sensitive Frontier Analysis for Feature Extraction
2009In this paper, an efficient feature extraction method, named local sensitive frontier analysis (LSFA), is proposed. LSFA tries to find instances near the crossing of the multi-manifold, which are sensitive to classification, to form the frontier automatically.
Chao Wang 0071 +2 more
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Local Sensitivity Analysis with Constraints
2017This chapter, which is our last on deterministic methods, addresses the removal of a typical assumption in sensitivity analysis.
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Large Scale Sentiment Analysis with Locality Sensitive BitHash
2015As social media data rapidly grows, sentiment analysis plays an increasingly more important role in classifying users’ opinions, attitudes and feelings expressed in text. However, most studies have been focused on the effectiveness of sentiment analysis, while ignoring the storage efficiency when processing large-scale high-dimensional text data.
Wenhao Zhang 0003 +4 more
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Increased sensitivity and localization in microspectral analysis of alloys
Journal of Applied Spectroscopy, 1967Methods of improving the sensitivity and localization are developed, which are based on restricting the discharge area with corundum rings. These two features are shown to be dependent on the shape and initial diameter of the ring, the duration of the discharge, and the parameters of the power supply.
B. G. Voronov, A. A. Samoilova
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Two-dimensional locality sensitive discriminant analysis
2008 International Conference on Wavelet Analysis and Pattern Recognition, 2008Recently, locality sensitive discriminant analysis (LSDA) was proposed for dimensionality reduction. As far as matrix data, such as images, they are often vectorized for LSDA algorithm to find the intrinsic manifold structure. Such a matrix-to-vector transform may cause the loss of some structural information residing in original 2D images.
null Yantao Wei +2 more
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