Feature-Selection Avrim Blum Department of Computer Science Carnegie Mellon...  Home/Search
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Abstract: Random projection is a simple technique that has had a number of applications in algorithm design. In the context of machine learning, it can provide insight into questions such as "why is a learning problem easier if data is separable by a large margin?" and "in what sense is choosing a kernel much like choosing a set of features?" This talk is intended to provide an introduction to random projection and to survey some simple learning algorithms and other applications to learning based... (Update)
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BibTeX entry: (Update)
@misc{ blum-random,
author = "Feature-Selection Avrim Blum",
title = "Random Projection, Margins, Kernels, and",
url = "citeseer.ist.psu.edu/752799.html" }
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63 Generalization performance of support vector machines and ot.. - Bartlett, Shawe-Taylor - 1999
50 Large margin classification using the Perceptron algorithm - Freund, Schapire - 1999
33 Experiments with random projection (context) - Dasgupta - 2000
32 The perceptron: A model for brain functioning (context) - Block - 1962
32 From on-line to batch learning (context) - Littlestone - 1989
32 Generalized Inverses: Theory and Applications (context) - Ben-Israel, Greville - 1974
25 Database-friendly random projections - Achlioptas - 2003
21 cient search for approximate nearest neighbor in high dimens.. (context) - Rabani, Kushilevitz et al. - 2000
3 Experiments with random projections for machine learning - Fradkin, Madigan - 2003
2 and low-dimensional mappings (context) - Balcan, Blum et al. - 2004
2 A PAC-style model for learning from labeled and unlabeled da.. - Balcan, Blum - 2005
1 a theory of kernels as similarity functions (context) - Balcan, Blum - 2006
1 robust concepts and random projection (context) - Arriaga, Vempala et al. - 1999
1 Face recognition experiments with random projection (context) - Goal, Bebis et al. - 2005
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