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» On learning with dissimilarity functions
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CVPR
2005
IEEE
14 years 11 months ago
Feature Kernel Functions: Improving SVMs Using High-Level Knowledge
Kernel functions are often cited as a mechanism to encode prior knowledge of a learning task. But it can be difficult to capture prior knowledge effectively. For example, we know ...
Qiang Sun, Gerald DeJong
KI
2007
Springer
14 years 3 months ago
Inductive Synthesis of Recursive Functional Programs
Abstract. We compare three systems for the task of synthesising functional recursive programs, namely Adate, an approach through evolutionary computation, the classification learn...
Martin Hofmann 0008, Andreas Hirschberger, Emanuel...
NIPS
1994
13 years 10 months ago
Combining Estimators Using Non-Constant Weighting Functions
This paper discusses the linearly weighted combination of estimators in which the weighting functions are dependent on the input. We show that the weighting functions can be deriv...
Volker Tresp, Michiaki Taniguchi
JMLR
2002
135views more  JMLR 2002»
13 years 9 months ago
Covering Number Bounds of Certain Regularized Linear Function Classes
Recently, sample complexity bounds have been derived for problems involving linear functions such as neural networks and support vector machines. In many of these theoretical stud...
Tong Zhang
ECAI
2004
Springer
14 years 1 months ago
Avoiding Data Overfitting in Scientific Discovery: Experiments in Functional Genomics
Functional genomics is a typical scientific discovery domain characterized by a very large number of attributes (genes) relative to the number of examples (observations). The dang...
Dragan Gamberger, Nada Lavrac