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» Feature selection in a kernel space
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FGR
2006
IEEE
147views Biometrics» more  FGR 2006»
14 years 4 months ago
Learning Sparse Features in Granular Space for Multi-View Face Detection
In this paper, a novel sparse feature set is introduced into the Adaboost learning framework for multi-view face detection (MVFD), and a learning algorithm based on heuristic sear...
Chang Huang, Haizhou Ai, Yuan Li, Shihong Lao
ECCV
2010
Springer
14 years 3 months ago
Building Compact Local Pairwise Codebook with Joint Feature Space Clustering
Abstract. This paper presents a simple, yet effective method of building a codebook for pairs of spatially close SIFT descriptors. Integrating such codebook into the popular bag-o...
ICASSP
2008
IEEE
14 years 4 months ago
Brute-forcing hierarchical functionals for paralinguistics: A waste of feature space?
While the ”‘quasi-state-of-the-art”’ towards acoustic emotion recognition relies on multivariate time-series analysis of e.g. pitch, energy, or MFCC by statistical functio...
Björn Schuller, Matthias Wimmer, Lorenz Moese...
NAACL
2007
13 years 11 months ago
Kernel Regression Based Machine Translation
We present a novel machine translation framework based on kernel regression techniques. In our model, the translation task is viewed as a string-to-string mapping, for which a reg...
Zhuoran Wang, John Shawe-Taylor, Sándor Sze...
ICML
1998
IEEE
14 years 10 months ago
Feature Selection via Concave Minimization and Support Vector Machines
Computational comparison is made between two feature selection approaches for nding a separating plane that discriminates between two point sets in an n-dimensional feature space ...
Paul S. Bradley, Olvi L. Mangasarian