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NIPS
2004
15 years 4 months ago
Maximising Sensitivity in a Spiking Network
We use unsupervised probabilistic machine learning ideas to try to explain the kinds of learning observed in real neurons, the goal being to connect abstract principles of self-or...
Anthony J. Bell, Lucas C. Parra
ICDE
2010
IEEE
212views Database» more  ICDE 2010»
15 years 3 months ago
Cleansing uncertain databases leveraging aggregate constraints
— Emerging uncertain database applications often involve the cleansing (conditioning) of uncertain databases using additional information as new evidence for reducing the uncerta...
Haiquan Chen, Wei-Shinn Ku, Haixun Wang
ALMOB
2006
155views more  ALMOB 2006»
15 years 3 months ago
Refining motifs by improving information content scores using neighborhood profile search
The main goal of the motif finding problem is to detect novel, over-represented unknown signals in a set of sequences (e.g. transcription factor binding sites in a genome). The mo...
Chandan K. Reddy, Yao-Chung Weng, Hsiao-Dong Chian...
NN
1998
Springer
177views Neural Networks» more  NN 1998»
15 years 3 months ago
Soft vector quantization and the EM algorithm
The relation between hard c-means (HCM), fuzzy c-means (FCM), fuzzy learning vector quantization (FLVQ), soft competition scheme (SCS) of Yair et al. (1992) and probabilistic Gaus...
Ethem Alpaydin
CVPR
2008
IEEE
16 years 5 months ago
Learning class-specific affinities for image labelling
Spectral clustering and eigenvector-based methods have become increasingly popular in segmentation and recognition. Although the choice of the pairwise similarity metric (or affin...
Dhruv Batra, Rahul Sukthankar, Tsuhan Chen