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COLT
2008
Springer
15 years 3 months ago
Does Unlabeled Data Provably Help? Worst-case Analysis of the Sample Complexity of Semi-Supervised Learning
We study the potential benefits to classification prediction that arise from having access to unlabeled samples. We compare learning in the semi-supervised model to the standard, ...
Shai Ben-David, Tyler Lu, Dávid Pál
NIPS
2008
15 years 2 months ago
On the Reliability of Clustering Stability in the Large Sample Regime
Clustering stability is an increasingly popular family of methods for performing model selection in data clustering. The basic idea is that the chosen model should be stable under...
Ohad Shamir, Naftali Tishby
102
Voted
GECCO
2005
Springer
158views Optimization» more  GECCO 2005»
15 years 6 months ago
A genetic algorithm approach to the selection of near-optimal subsets from large sets
The problem attempted in this paper is to select a sample from a large set where the sample is required to have a particular average property. The problem can be expressed as an o...
P. Whiting, P. W. Poon, J. N. Carter
ICDM
2003
IEEE
126views Data Mining» more  ICDM 2003»
15 years 6 months ago
Mining Relevant Text from Unlabelled Documents
Automatic classification of documents is an important area of research with many applications in the fields of document searching, forensics and others. Methods to perform class...
Daniel Barbará, Carlotta Domeniconi, Ning K...
ECCV
2006
Springer
16 years 3 months ago
Sampling Representative Examples for Dimensionality Reduction and Recognition - Bootstrap Bumping LDA
Abstract. We present a novel method for dimensionality reduction and recognition based on Linear Discriminant Analysis (LDA), which specifically deals with the Small Sample Size (S...
Hui Gao, James W. Davis