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ESSMAC
2003
Springer
15 years 9 months ago
Filtered Gaussian Processes for Learning with Large Data-Sets
Kernel-based non-parametric models have been applied widely over recent years. However, the associated computational complexity imposes limitations on the applicability of those me...
Jian Qing Shi, Roderick Murray-Smith, D. M. Titter...
AI
2001
Springer
15 years 9 months ago
A Case Study for Learning from Imbalanced Data Sets
We present our experience in applying a rule induction technique to an extremely imbalanced pharmaceutical data set. We focus on using a variety of performance measures to evaluate...
Aijun An, Nick Cercone, Xiangji Huang
AIEDAM
1998
87views more  AIEDAM 1998»
15 years 4 months ago
Learning to set up numerical optimizations of engineering designs
Gradient-based numerical optimization of complex engineering designs offers the promise of rapidly producing better designs. However, such methods generally assume that the object...
Mark Schwabacher, Thomas Ellman, Haym Hirsh
CVPR
2010
IEEE
16 years 28 days ago
Optimizing One-Shot Recognition with Micro-Set Learning
For object category recognition to scale beyond a small number of classes, it is important that algorithms be able to learn from a small amount of labeled data per additional clas...
Kevin Tang, Marshall Tappen, Rahul Sukthankar, Chr...
SDL
2007
152views Hardware» more  SDL 2007»
15 years 6 months ago
TTCN-3 Quality Engineering: Using Learning Techniques to Evaluate Metric Sets
Software metrics are an essential means to assess software quality. For the assessment of software quality, typically sets of complementing metrics are used since individual metric...
Edith Werner, Jens Grabowski, Helmut Neukirchen, N...