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» Training of Support Vector Machines with Mahalanobis Kernels
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TSD
2010
Springer
15 years 4 months ago
Correlation Features and a Linear Transform Specific Reproducing Kernel
Abstract. In this paper we introduce two ideas for phoneme classification: First, we derive the necessary steps to integrate linear transform into the computation of reproducing ke...
Andreas Beschorner, Dietrich Klakow
CORR
2006
Springer
130views Education» more  CORR 2006»
15 years 6 months ago
Genetic Programming for Kernel-based Learning with Co-evolving Subsets Selection
Abstract. Support Vector Machines (SVMs) are well-established Machine Learning (ML) algorithms. They rely on the fact that i) linear learning can be formalized as a well-posed opti...
Christian Gagné, Marc Schoenauer, Mich&egra...
COLING
2008
15 years 7 months ago
A Hybrid Generative/Discriminative Framework to Train a Semantic Parser from an Un-annotated Corpus
We propose a hybrid generative/discriminative framework for semantic parsing which combines the hidden vector state (HVS) model and the hidden Markov support vector machines (HMSV...
Deyu Zhou, Yulan He
ICASSP
2010
IEEE
15 years 4 months ago
A comparison of approaches for modeling prosodic features in speaker recognition
Prosodic information has been successfully used for speaker recognition for more than a decade. The best-performing prosodic system to date has been one based on features extracte...
Luciana Ferrer, Nicolas Scheffer, Elizabeth Shribe...
JMLR
2006
131views more  JMLR 2006»
15 years 6 months ago
Incremental Support Vector Learning: Analysis, Implementation and Applications
Incremental Support Vector Machines (SVM) are instrumental in practical applications of online learning. This work focuses on the design and analysis of efficient incremental SVM ...
Pavel Laskov, Christian Gehl, Stefan Krüger, ...