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DAGM
2004
Springer
15 years 9 months ago
Learning from Labeled and Unlabeled Data Using Random Walks
We consider the general problem of learning from labeled and unlabeled data. Given a set of points, some of them are labeled, and the remaining points are unlabeled. The goal is to...
Dengyong Zhou, Bernhard Schölkopf
ICDM
2008
IEEE
118views Data Mining» more  ICDM 2008»
15 years 10 months ago
Dirichlet Process Based Evolutionary Clustering
Evolutionary Clustering has emerged as an important research topic in recent literature of data mining, and solutions to this problem have found a wide spectrum of applications, p...
Tianbing Xu, Zhongfei (Mark) Zhang, Philip S. Yu, ...
ACSW
2004
15 years 5 months ago
Early Assessment of Classification Performance
The ability to distinguish between objects is the fundamental to learning and intelligent behavior in general. The difference between two things is the information we seek; the pr...
Bostjan Brumen, Izidor Golob, Hannu Jaakkola, Tatj...
ICML
2007
IEEE
16 years 4 months ago
Hierarchical Gaussian process latent variable models
The Gaussian process latent variable model (GP-LVM) is a powerful approach for probabilistic modelling of high dimensional data through dimensional reduction. In this paper we ext...
Neil D. Lawrence, Andrew J. Moore
ICML
2004
IEEE
16 years 4 months ago
A spatio-temporal extension to Isomap nonlinear dimension reduction
We present an extension of Isomap nonlinear dimension reduction (Tenenbaum et al., 2000) for data with both spatial and temporal relationships. Our method, ST-Isomap, augments the...
Odest Chadwicke Jenkins, Maja J. Mataric