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» A Theory for Memory-Based Learning
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NIPS
2007
15 years 7 months ago
Discriminative K-means for Clustering
We present a theoretical study on the discriminative clustering framework, recently proposed for simultaneous subspace selection via linear discriminant analysis (LDA) and cluster...
Jieping Ye, Zheng Zhao, Mingrui Wu
NIPS
2007
15 years 7 months ago
Consistent Minimization of Clustering Objective Functions
Clustering is often formulated as a discrete optimization problem. The objective is to find, among all partitions of the data set, the best one according to some quality measure....
Ulrike von Luxburg, Sébastien Bubeck, Stefa...
AAAI
2006
15 years 7 months ago
From the Programmer's Apprentice to Human-Robot Interaction: Thirty Years of Research on Human-Computer Collaboration
We summarize the continuous thread of research we have conducted over the past thirty years on human-computer collaboration. This research reflects many of the themes and issues i...
Charles Rich, Candace L. Sidner
DMIN
2006
134views Data Mining» more  DMIN 2006»
15 years 7 months ago
Hyper-Rectangular and k-Nearest-Neighbor Models in Stochastic Discrimination
The stochastic discrimination (SD) theory considers learning as building models of uniform coverage over data distributions. Despite successful trials of the derived SD method in s...
Iryna Skrypnyk, Tin Kam Ho
FLAIRS
2004
15 years 7 months ago
Multimodal Function Optimization Using Local Ruggedness Information
In multimodal function optimization, niching techniques create diversification within the population, thus encouraging heterogeneous convergence. The key to the effective diversif...
Jian Zhang 0007, Xiaohui Yuan, Bill P. Buckles