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CVPR
2007
IEEE
16 years 1 months ago
Matrix-Structural Learning (MSL) of Cascaded Classifier from Enormous Training Set
Aiming at the problem when both positive and negative training set are enormous, this paper proposes a novel Matrix-Structural Learning (MSL) method, as an extension to Viola and ...
Shengye Yan, Shiguang Shan, Xilin Chen, Wen Gao, J...
MM
2005
ACM
134views Multimedia» more  MM 2005»
16 years 6 days ago
Graph based multi-modality learning
To better understand the content of multimedia, a lot of research efforts have been made on how to learn from multi-modal feature. In this paper, it is studied from a graph point ...
Hanghang Tong, Jingrui He, Mingjing Li, Changshui ...
SGAI
2005
Springer
16 years 4 days ago
The Effect of Principal Component Analysis on Machine Learning Accuracy with High Dimensional Spectral Data
This paper presents the results of an investigation into the use of machine learning methods for the identification of narcotics from Raman spectra. The classification of spectr...
Tom Howley, Michael G. Madden, Marie-Louise O'Conn...
ICML
2004
IEEE
16 years 2 days ago
Learning a kernel matrix for nonlinear dimensionality reduction
We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into ...
Kilian Q. Weinberger, Fei Sha, Lawrence K. Saul
FOCS
1999
IEEE
15 years 11 months ago
An Algorithmic Theory of Learning: Robust Concepts and Random Projection
We study the phenomenon of cognitive learning from an algorithmic standpoint. How does the brain effectively learn concepts from a small number of examples despite the fact that e...
Rosa I. Arriaga, Santosh Vempala