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126
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GECCO
2006
Springer
144views Optimization» more  GECCO 2006»
15 years 7 months ago
On semi-supervised clustering via multiobjective optimization
Semi-supervised classification uses aspects of both unsupervised and supervised learning to improve upon the performance of traditional classification methods. Semi-supervised clu...
Julia Handl, Joshua D. Knowles
125
Voted
SIGSOFT
2005
ACM
16 years 4 months ago
Facilitating software evolution research with kenyon
Software evolution research inherently has several resourceintensive logistical constraints. Archived project artifacts, such as those found in source code repositories and bug tr...
Jennifer Bevan, E. James Whitehead Jr., Sunghun Ki...
99
Voted
AGENTS
2000
Springer
15 years 7 months ago
Towards a fault-tolerant multi-agent system architecture
Multi-agent systems are prone to failures typical of any distributed system. Agents and resources may become unavailable due to machine crashes, communication breakdowns, process ...
Sanjeev Kumar, Philip R. Cohen
136
Voted
WSDM
2010
ACM
322views Data Mining» more  WSDM 2010»
16 years 25 days ago
Inferring Search Behaviors Using Partially Observable Markov (POM) Model
This article describes an application of the partially observable Markov (POM) model to the analysis of a large scale commercial web search log. Mathematically, POM is a variant o...
Kuansan Wang, Nikolas Gloy, Xiaolong Li
144
Voted
SBCCI
2003
ACM
129views VLSI» more  SBCCI 2003»
15 years 8 months ago
Hyperspectral Images Clustering on Reconfigurable Hardware Using the K-Means Algorithm
Unsupervised clustering is a powerful technique for understanding multispectral and hyperspectral images, being k-means one of the most used iterative approaches. It is a simple th...
Abel Guilhermino S. Filho, Alejandro César ...