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PR
2007
104views more  PR 2007»
13 years 7 months ago
Optimizing resources in model selection for support vector machine
Tuning SVM hyperparameters is an important step in achieving a high-performance learning machine. It is usually done by minimizing an estimate of generalization error based on the...
Mathias M. Adankon, Mohamed Cheriet
HAPTICS
2009
IEEE
13 years 11 months ago
Computationally efficient techniques for data-driven haptic rendering
Data-driven haptic rendering requires processing of raw recorded signals, which leads to high computational effort for large datasets. To achieve real-time performance, one possib...
Raphael Höver, Massimiliano Di Luca, Gá...
TIP
2008
175views more  TIP 2008»
13 years 7 months ago
Customizing Kernel Functions for SVM-Based Hyperspectral Image Classification
Previous research applying kernel methods such as support vector machines (SVMs) to hyperspectral image classification has achieved performance competitive with the best available ...
Baofeng Guo, Steve R. Gunn, Robert I. Damper, Jame...
IMC
2007
ACM
13 years 9 months ago
On optimal probing for delay and loss measurement
Packet delay and loss are two fundamental measures of performance. Using active probing to measure delay and loss typically involves sending Poisson probes, on the basis of the PA...
François Baccelli, Sridhar Machiraju, Darry...
NN
1998
Springer
177views Neural Networks» more  NN 1998»
13 years 7 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