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» Boruta - A System for Feature Selection
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FGR
2006
IEEE
147views Biometrics» more  FGR 2006»
15 years 3 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
ICPR
2010
IEEE
15 years 2 months ago
Adaptive Feature and Score Level Fusion Strategy Using Genetic Algorithms
Classifier fusion is considered as one of the best strategies for improving performances upon general purpose classification systems. On the other hand, fusion strategy space stro...
Wael Ben Soltana, Mohsen Ardabilian, Liming Chen, ...
ICVGIP
2004
14 years 11 months ago
Cursive Word Recognition Using a Novel Feature Extraction Method and a Neural Network
In this paper, we present a holistic system for the recognition of cursive handwriting that utilizes a novel feature extraction method and a neural network. The Hough transform is...
José Ruiz-Pinales, René Jaime-Rivas
ICASSP
2010
IEEE
14 years 10 months ago
Boosted binary features for noise-robust speaker verification
The standard approach to speaker verification is to extract cepstral features from the speech spectrum and model them by generative or discriminative techniques. We propose a nov...
Anindya Roy, Mathew Magimai-Doss, Sébastien...
PR
2006
88views more  PR 2006»
14 years 9 months ago
Directional features in online handwriting recognition
The selection of valuable features is crucial in pattern recognition. In this paper we deal with the issue that part of features originate from directional instead of common linea...
Claus Bahlmann