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» Training of Classifiers Using Virtual Samples Only
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PAMI
2006
205views more  PAMI 2006»
13 years 7 months ago
Learning Outdoor Color Classification
We present an algorithm for color classification with explicit illuminant estimation and compensation. A Gaussian classifier is trained with color samples from just one training im...
Roberto Manduchi
KDD
2002
ACM
160views Data Mining» more  KDD 2002»
14 years 8 months ago
Scaling multi-class support vector machines using inter-class confusion
Support vector machines (SVMs) excel at two-class discriminative learning problems. They often outperform generative classifiers, especially those that use inaccurate generative m...
Shantanu Godbole, Sunita Sarawagi, Soumen Chakraba...
CVPR
2010
IEEE
14 years 28 days ago
Learning Appearance in Virtual Scenarios for Pedestrian Detection
Detecting pedestrians in images is a key functionality to avoid vehicle-to-pedestrian collisions. The most promising detectors rely on appearance-based pedestrian classifiers tra...
Francisco Marin Tur, David Vazquez, David Geronimo...
NIPS
2007
13 years 9 months ago
Learning to classify complex patterns using a VLSI network of spiking neurons
We propose a compact, low power VLSI network of spiking neurons which can learn to classify complex patterns of mean firing rates on–line and in real–time. The network of int...
Srinjoy Mitra, Giacomo Indiveri, Stefano Fusi
ICDM
2003
IEEE
210views Data Mining» more  ICDM 2003»
14 years 27 days ago
CBC: Clustering Based Text Classification Requiring Minimal Labeled Data
Semi-supervised learning methods construct classifiers using both labeled and unlabeled training data samples. While unlabeled data samples can help to improve the accuracy of trai...
Hua-Jun Zeng, Xuanhui Wang, Zheng Chen, Hongjun Lu...