Calibration as parameter estimation in sensor networks
WSNA '02 Proceedings of the 1st ACM international workshop on Wireless sensor networks and applications
Journal of Optimization Theory and Applications
Macro-calibration in sensor/actuator networks
Mobile Networks and Applications
A Regression Model for Fuzzy Initial Data
Automation and Remote Control
Evaluation strategies for image understanding and retrieval
Proceedings of the 7th ACM SIGMM international workshop on Multimedia information retrieval
A study of partial F tests for multiple linear regression models
Computational Statistics & Data Analysis
Human visual system based adaptive digital image watermarking
Signal Processing
A clique algorithm for standard quadratic programming
Discrete Applied Mathematics
A new class of invertible FIR filters for spectral shaping
Signal Processing
A quadratic programming approach to blind equalization and signal separation
IEEE Transactions on Signal Processing
Propagation of structural uncertainty to linear aeroelastic stability
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A pattern recognition based approach to consistency analysis of geophysical datasets
Computers & Geosciences
Affine iterative closest point algorithm for point set registration
Pattern Recognition Letters
Apparent display resolution enhancement for moving images
ACM SIGGRAPH 2010 papers
A sequential minimal optimization algorithm for the all-distances support vector machine
CIARP'10 Proceedings of the 15th Iberoamerican congress conference on Progress in pattern recognition, image analysis, computer vision, and applications
QoS-based cooperative algorithm for integral multi-commodity flow problem
Computer Communications
Shape space exploration of constrained meshes
Proceedings of the 2011 SIGGRAPH Asia Conference
Polarization fields: dynamic light field display using multi-layer LCDs
Proceedings of the 2011 SIGGRAPH Asia Conference
A novel SVM+NDA model for classification with an application to face recognition
Pattern Recognition
Computation of Minimum Energy Paths for Quasi-Linear Problems
Journal of Scientific Computing
Collective prediction with latent graphs
Proceedings of the 20th ACM international conference on Information and knowledge management
Robust visual reranking via sparsity and ranking constraints
MM '11 Proceedings of the 19th ACM international conference on Multimedia
Fast high-resolution appearance editing using superimposed projections
ACM Transactions on Graphics (TOG)
Improving vehicle aeroacoustics using machine learning
Engineering Applications of Artificial Intelligence
The truncated Stieltjes moment problem solved by using kernel density functions
Journal of Computational and Applied Mathematics
Optimization-Based modeling with applications to transport: part 2. the optimization algorithm
LSSC'11 Proceedings of the 8th international conference on Large-Scale Scientific Computing
Shape and Refractive Index from Single-View Spectro-Polarimetric Images
International Journal of Computer Vision
Opacity optimization for 3D line fields
ACM Transactions on Graphics (TOG) - SIGGRAPH 2013 Conference Proceedings
Information diffusion in online social networks: a survey
ACM SIGMOD Record
Stable local volatility function calibration using spline kernel
Computational Optimization and Applications
Optimal calculation of tensor learning approaches
ISNN'13 Proceedings of the 10th international conference on Advances in Neural Networks - Volume Part I
Intrinsic dimension estimation via nearest constrained subspace classifier
Pattern Recognition
Special Section on Advanced Displays: Display adaptive 3D content remapping
Computers and Graphics
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We propose a new algorithm, a reflective Newton method, for the minimization of a quadratic function of many variables subject to upper and lower bounds on some of the variables. The method applies to a general (indefinite) quadratic function for which a local minimizer subject to bounds is required and is particularly suitable for the large-scale problem. Our new method exhibits strong convergence properties and global and second-order convergence and appears to have significant practical potential. Strictly feasible points are generated. We provide experimental results on moderately large and sparse problems based on both sparse Cholesky and preconditioned conjugate gradient linear solvers.