Results 271 to 280 of about 1,015,464 (310)

A Novel Local Sensitive Frontier Analysis for Feature Extraction

2009
In 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, De-Shuang Huang, Bo Li 0002   +2 more
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Large Scale Sentiment Analysis with Locality Sensitive BitHash

2015
As 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, Jianqiu Ji, Jun Zhu 0001, Hua Xu 0003, Bo Zhang 0010   +4 more
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Increased sensitivity and localization in microspectral analysis of alloys

Journal of Applied Spectroscopy, 1967
Methods 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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Sensitivity analysis of parallel applications to local and non-local interference

2007
The environment in which a parallel application is executed has high impact on the performance of the application due to interference caused by various factors in the execution environment. A detailed understanding of the sensitivity of the application to the parameters describing the execution environment can be of great help in (a) predicting a ...
Vaddadi P. Chandu, Karandeep Singh
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Sensitivity analysis with respect to a local perturbation of the material property

Asymptotic Analysis, 2006
In the present work, the notion of topological sensitivity is extended to the case of a local perturbation of the properties of the material constitutive of the domain. As a model example, we consider the problem −div(α ε A∇u ε )+β ε
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Local Sensitivity Analysis and Matrix Derivatives

1982
This paper discusses two types of matrix by matrix derivatives; the B-type derivative, introduced by Balestra (1976) and the new defined A-type derivative, because of the arrangement in a “anti-Kronecker” type fashion. Both types of derivatives are linked by permutation matrices (also called commutation matrices by Magnus & Neudecker (1979)), a special
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Adjoint-Based Local Sensitivity Analysis

2018
This chapter introduces the adjoint operator equations for local sensitivity analysis, an intrusive method for sensitivity analysis. Section 6.1 introduces the adjoint operator and demonstrates that for a somewhat general class of QoIs, a QoI can be written as an inner product of the adjoint equations.
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Geometric sensitivity analysis with local coordinate transformations

Communications in Applied Numerical Methods, 1988
AbstractThe dimensionality of truss, beam, membrane and shell finite elements is often less than that of the global co‐ordinate system, and element calculations must be performed in local co‐ordinates. Design parameters which affect the nodal co‐ordinates in such elements control both the element dimensions and orientation, and element design ...
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A fuzzy and locally sensitive method for cluster analysis

Proceedings of Third Australian and New Zealand Conference on Intelligent Information Systems. ANZIIS-95, 2002
Cluster analysis has been playing an important role in pattern recognition, image processing and time series analysis. The majority of the existing clustering algorithms depend on initial parameters and assumptions about the underlying data structure. A fuzzy method of mode separation is proposed.
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Local Sensitivity Analysis Based on Derivative Approximations

2018
This is the first of three successive chapters on the topic of sensitivity analysis, i.e., determining which variables a scalar quantity of interest depends on most strongly. Chapter explores using derivatives as local indicators of sensitivity based on finite difference approximations of first and second-order Taylor expansions.
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