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» Multiple Kernel Learning with High Order Kernels
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ML
2010
ACM
181views Machine Learning» more  ML 2010»
13 years 6 months ago
Decomposing the tensor kernel support vector machine for neuroscience data with structured labels
Abstract The tensor kernel has been used across the machine learning literature for a number of purposes and applications, due to its ability to incorporate samples from multiple s...
David R. Hardoon, John Shawe-Taylor
TIP
2011
217views more  TIP 2011»
13 years 2 months ago
Contextual Object Localization With Multiple Kernel Nearest Neighbor
—Recently, many object localization models have shown that incorporating contextual cues can greatly improve accuracy over using appearance features alone. Therefore, many of the...
Brian McFee, Carolina Galleguillos, Gert R. G. Lan...
ML
2002
ACM
223views Machine Learning» more  ML 2002»
13 years 7 months ago
Text Categorization with Support Vector Machines. How to Represent Texts in Input Space?
The choice of the kernel function is crucial to most applications of support vector machines. In this paper, however, we show that in the case of text classification, term-frequenc...
Edda Leopold, Jörg Kindermann
ICASSP
2009
IEEE
13 years 5 months ago
Volterra series for analyzing MLP based phoneme posterior estimator
We present a framework to apply Volterra series to analyze multilayered perceptrons trained to estimate the posterior probabilities of phonemes in automatic speech recognition. Th...
Joel Pinto, Garimella S. V. S. Sivaram, Hynek Herm...
FCCM
2005
IEEE
106views VLSI» more  FCCM 2005»
14 years 1 months ago
High-Performance FPGA-Based General Reduction Methods
FPGA-based floating-point kernels must exploit algorithmic parallelism and use deeply pipelined cores to gain a performance advantage over general-purpose processors. Inability t...
Gerald R. Morris, Ling Zhuo, Viktor K. Prasanna