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ICPR
2006
IEEE
14 years 8 months ago
Mixture of Support Vector Machines for HMM based Speech Recognition
Speech recognition is usually based on Hidden Markov Models (HMMs), which represent the temporal dynamics of speech very efficiently, and Gaussian mixture models, which do non-opt...
Sven E. Krüger, Martin Schafföner, Marce...
CAIP
2009
Springer
210views Image Analysis» more  CAIP 2009»
13 years 11 months ago
Shape Classification Using a Flexible Graph Kernel
The medial axis being an homotopic transformation, the skeleton of a 2D shape corresponds to a planar graph having one face for each hole of the shape and one node for each junctio...
François-Xavier Dupé, Luc Brun
ICML
2006
IEEE
14 years 8 months ago
The support vector decomposition machine
In machine learning problems with tens of thousands of features and only dozens or hundreds of independent training examples, dimensionality reduction is essential for good learni...
Francisco Pereira, Geoffrey J. Gordon
ICPR
2002
IEEE
14 years 8 months ago
A Computationally Efficient Approach to Indoor/Outdoor Scene Classification
Prior research in scene classification has shown that high-level information can be inferred from low-level image features. Classification rates of roughly 90% have been reported ...
Navid Serrano, Andreas E. Savakis, Jiebo Luo
ICASSP
2009
IEEE
13 years 11 months ago
High-level feature extraction using SVM with walk-based graph kernel
We investigate a method using support vector machines (SVMs) with walk-based graph kernels for high-level feature extraction from images. In this method, each image is first segme...
Jean-Philippe Vert, Tomoko Matsui, Shin'ichi Satoh...