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KDD
2008
ACM
172views Data Mining» more  KDD 2008»
16 years 7 months ago
Structured metric learning for high dimensional problems
The success of popular algorithms such as k-means clustering or nearest neighbor searches depend on the assumption that the underlying distance functions reflect domain-specific n...
Jason V. Davis, Inderjit S. Dhillon
GECCO
2006
Springer
162views Optimization» more  GECCO 2006»
15 years 10 months ago
Evolutionary learning with kernels: a generic solution for large margin problems
In this paper we embed evolutionary computation into statistical learning theory. First, we outline the connection between large margin optimization and statistical learning and s...
Ingo Mierswa
ESANN
2004
15 years 8 months ago
Neural networks for data mining: constrains and open problems
When we talk about using neural networks for data mining we have in mind the original data mining scope and challenge. How did neural networks meet this challenge? Can we run neura...
Razvan Andonie, Boris Kovalerchuk
184
Voted
AIEDU
2010
15 years 1 months ago
Scaffolding Meta-Cognitive Skills for Effective Analogical Problem Solving via Tailored Example Selection
Although worked-out examples play a key role in cognitive skill acquisition, research demonstrates that students have various levels of meta-cognitive abilities for using examples ...
Kasia Muldner, Cristina Conati
SIAMSC
2008
206views more  SIAMSC 2008»
15 years 6 months ago
The Compact Discontinuous Galerkin (CDG) Method for Elliptic Problems
We present a compact discontinuous Galerkin (CDG) method for an elliptic model problem. The problem is first cast as a system of first order equations by introducing the gradient o...
Jaime Peraire, Per-Olof Persson