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» Learning from Highly Structured Data by Decomposition
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BMCBI
2007
200views more  BMCBI 2007»
14 years 10 months ago
Assessment of algorithms for high throughput detection of genomic copy number variation in oligonucleotide microarray data
Background: Genomic deletions and duplications are important in the pathogenesis of diseases, such as cancer and mental retardation, and have recently been shown to occur frequent...
Ágnes Baross, Allen D. Delaney, H. Irene Li...
WWW
2004
ACM
15 years 10 months ago
Ontological representation of learning objects: building interoperable vocabulary and structures
The ontological representation of learning objects is a way to deal with the interoperability and reusability of learning objects (including metadata) through providing a semantic...
Jian Qin, Naybell Hernández
BMCBI
2011
14 years 1 months ago
Learning sparse models for a dynamic Bayesian network classifier of protein secondary structure
Background: Protein secondary structure prediction provides insight into protein function and is a valuable preliminary step for predicting the 3D structure of a protein. Dynamic ...
Zafer Aydin, Ajit Singh, Jeff Bilmes, William Staf...
CVPR
2005
IEEE
15 years 12 months ago
Robust L1 Norm Factorization in the Presence of Outliers and Missing Data by Alternative Convex Programming
Matrix factorization has many applications in computer vision. Singular Value Decomposition (SVD) is the standard algorithm for factorization. When there are outliers and missing ...
Qifa Ke, Takeo Kanade
DEXA
2006
Springer
193views Database» more  DEXA 2006»
15 years 1 months ago
Understanding and Enhancing the Folding-In Method in Latent Semantic Indexing
Abstract. Latent Semantic Indexing(LSI) has been proved to be effective to capture the semantic structure of document collections. It is widely used in content-based text retrieval...
Xiang Wang 0002, Xiaoming Jin