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» Maximal Vector Computation in Large Data Sets
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CVPR
2008
IEEE
14 years 11 months ago
Spectral methods for semi-supervised manifold learning
Given a finite number of data points sampled from a low-dimensional manifold embedded in a high dimensional space together with the parameter vectors for a subset of the data poin...
Zhenyue Zhang, Hongyuan Zha, Min Zhang
KDD
2000
ACM
153views Data Mining» more  KDD 2000»
14 years 26 days ago
The generalized Bayesian committee machine
In this paper we introduce the Generalized Bayesian Committee Machine (GBCM) for applications with large data sets. In particular, the GBCM can be used in the context of kernel ba...
Volker Tresp
PRESENCE
2006
70views more  PRESENCE 2006»
13 years 9 months ago
Learning, Experience, and Cognitive Factors in the Presence Experiences of Gamers: An Exploratory Relational Study
This paper presents a large scale (N 101) exploratory relational study of computer gamers' gaming habits and their presence experiences. The study posited and examined the ef...
David Nunez, Edwin H. Blake
BMCBI
2008
114views more  BMCBI 2008»
13 years 9 months ago
Visualizing and clustering high throughput sub-cellular localization imaging
Background: The expansion of automatic imaging technologies has created a need to be able to efficiently compare and review large sets of image data. To enable comparisons of imag...
Nicholas A. Hamilton, Rohan D. Teasdale
PR
2008
102views more  PR 2008»
13 years 9 months ago
Classification in an informative sample subspace
We have developed an informative sample subspace (ISS) method that is suitable for projecting high-dimensional data onto a low-dimensional subspace for classification purposes. In...
Guoping Qiu, Jianzhong Fang