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ICDM
2006
IEEE
225views Data Mining» more  ICDM 2006»
15 years 10 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
15 years 5 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
14 years 11 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
15 years 10 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»
15 years 4 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