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» Feature selection in a kernel space
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FGR
2006
IEEE
147views Biometrics» more  FGR 2006»
15 years 5 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
15 years 4 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
15 years 6 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
15 years 1 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
16 years 18 days 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