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NIPS
2004
14 years 11 months ago
Learning Hyper-Features for Visual Identification
We address the problem of identifying specific instances of a class (cars) from a set of images all belonging to that class. Although we cannot build a model for any particular in...
Andras Ferencz, Erik G. Learned-Miller, Jitendra M...
SDM
2007
SIAM
137views Data Mining» more  SDM 2007»
14 years 11 months ago
Semi-supervised Feature Selection via Spectral Analysis
Feature selection is an important task in effective data mining. A new challenge to feature selection is the so-called “small labeled-sample problem” in which labeled data is...
Zheng Zhao, Huan Liu
SRDS
2010
IEEE
14 years 7 months ago
Invariants Based Failure Diagnosis in Distributed Computing Systems
This paper presents an instance based approach to diagnosing failures in computing systems. Owing to the fact that a large portion of occurred failures are repeated ones, our meth...
Haifeng Chen, Guofei Jiang, Kenji Yoshihira, Akhil...
UAI
2004
14 years 11 months ago
Active Model Selection
Classical learning assumes the learner is given a labeled data sample, from which it learns a model. The field of Active Learning deals with the situation where the learner begins...
Omid Madani, Daniel J. Lizotte, Russell Greiner
JMLR
2006
99views more  JMLR 2006»
14 years 9 months ago
Worst-Case Analysis of Selective Sampling for Linear Classification
A selective sampling algorithm is a learning algorithm for classification that, based on the past observed data, decides whether to ask the label of each new instance to be classi...
Nicolò Cesa-Bianchi, Claudio Gentile, Luca ...