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ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
14 years 1 months ago
Adaptive Kernel Principal Component Analysis with Unsupervised Learning of Kernels
Choosing an appropriate kernel is one of the key problems in kernel-based methods. Most existing kernel selection methods require that the class labels of the training examples ar...
Daoqiang Zhang, Zhi-Hua Zhou, Songcan Chen
SPLC
2008
13 years 8 months ago
Filtered Cartesian Flattening: An Approximation Technique for Optimally Selecting Features while Adhering to Resource Constraint
Software Product-lines (SPLs) use modular software components that can be reconfigured into different variants for different requirements sets. Feature modeling is a common method...
Jules White, B. Doughtery, Douglas C. Schmidt
TSP
2010
13 years 2 months ago
Variance-component based sparse signal reconstruction and model selection
We propose a variance-component probabilistic model for sparse signal reconstruction and model selection. The measurements follow an underdetermined linear model, where the unknown...
Kun Qiu, Aleksandar Dogandzic
ICDCS
2005
IEEE
14 years 1 months ago
Optimal Component Composition for Scalable Stream Processing
Stream processing has become increasingly important with emergence of stream applications such as audio/video surveillance, stock price tracing, and sensor data analysis. A challe...
Xiaohui Gu, Philip S. Yu, Klara Nahrstedt
MTA
2000
165views more  MTA 2000»
13 years 7 months ago
Approximating Content-Based Object-Level Image Retrieval
Object-level image retrieval is an active area of research. Given an image, a human observerdoesnot see randomdots of colors. Rather,he she observesfamiliarobjectsin the image. The...
Wynne Hsu, Tat-Seng Chua, Hung Keng Pung