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» Contention-Sensitive Data Structures and Algorithms
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GECCO
2009
Springer
188views Optimization» more  GECCO 2009»
15 years 10 months ago
Exploiting multiple classifier types with active learning
Many approaches to active learning involve training one classifier by periodically choosing new data points about which the classifier has the least confidence, but designing a co...
Zhenyu Lu, Josh Bongard
AAAI
2007
15 years 9 months ago
Isometric Projection
Recently the problem of dimensionality reduction has received a lot of interests in many fields of information processing. We consider the case where data is sampled from a low d...
Deng Cai, Xiaofei He, Jiawei Han
ICDM
2005
IEEE
151views Data Mining» more  ICDM 2005»
16 years 13 days ago
A Framework for Semi-Supervised Learning Based on Subjective and Objective Clustering Criteria
In this paper, we propose a semi-supervised framework for learning a weighted Euclidean subspace, where the best clustering can be achieved. Our approach capitalizes on user-const...
Maria Halkidi, Dimitrios Gunopulos, Nitin Kumar, M...
NAACL
2007
15 years 8 months ago
Relationship between Non-Projective Edges, Their Level Types, and Well-Nestedness
Dependency analysis of natural language gives rise to non-projective structures. The constraint of well-nestedness on dependency trees has been recently shown to give a good fit ...
Jirí Havelka
CORR
2010
Springer
120views Education» more  CORR 2010»
15 years 1 months ago
LRM-Trees: Compressed Indices, Adaptive Sorting, and Compressed Permutations
LRM-Trees are an elegant way to partition a sequence of values into sorted consecutive blocks, and to express the relative position of the first element of each block within a prev...
Jérémy Barbay, Johannes Fischer