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CORR
2006
Springer
153views Education» more  CORR 2006»
15 years 3 months ago
Genetic Programming, Validation Sets, and Parsimony Pressure
Fitness functions based on test cases are very common in Genetic Programming (GP). This process can be assimilated to a learning task, with the inference of models from a limited n...
Christian Gagné, Marc Schoenauer, Marc Pari...
ICPR
2006
IEEE
16 years 4 months ago
Enhancing Training Set for Face Detection
We present a novel method to enhance training set for face detection with nonlinearly generated examples from the original data. The motivation is from Support Vector Machines (SV...
Ruiping Wang, Jie Chen, Shiguang Shan, Wen Gao
ICML
2009
IEEE
16 years 4 months ago
SimpleNPKL: simple non-parametric kernel learning
Previous studies of Non-Parametric Kernel (NPK) learning usually reduce to solving some Semi-Definite Programming (SDP) problem by a standard SDP solver. However, time complexity ...
Jinfeng Zhuang, Ivor W. Tsang, Steven C. H. Hoi
ICPR
2008
IEEE
16 years 4 months ago
SVMs, Gaussian mixtures, and their generative/discriminative fusion
We present a new technique that employs support vector machines and Gaussian mixture densities to create a generative/discriminative joint classifier. In the past, several approac...
Georg Heigold, Hermann Ney, Thomas Deselaers
153
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ML
2002
ACM
163views Machine Learning» more  ML 2002»
15 years 3 months ago
Structural Modelling with Sparse Kernels
A widely acknowledged drawback of many statistical modelling techniques, commonly used in machine learning, is that the resulting model is extremely difficult to interpret. A numb...
Steve R. Gunn, Jaz S. Kandola