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» On the Complexity of Function Learning
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VTS
2006
IEEE
108views Hardware» more  VTS 2006»
15 years 9 months ago
Bridging the Accuracy of Functional and Machine-Learning-Based Mixed-Signal Testing
Abstract— Numerous machine-learning-based test methodologies have been proposed in recent years as a fast alternative to the standard functional testing of mixed-signal/RF integr...
Haralampos-G. D. Stratigopoulos, Yiorgos Makris
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
15 years 8 months ago
Preventing overfitting in GP with canary functions
Overfitting is a fundamental problem of most machine learning techniques, including genetic programming (GP). Canary functions have been introduced in the literature as a concept ...
Nate Foreman, Matthew P. Evett
ACL
2006
15 years 4 months ago
Using Machine-Learning to Assign Function Labels to Parser Output for Spanish
Data-driven grammatical function tag assignment has been studied for English using the Penn-II Treebank data. In this paper we address the question of whether such methods can be ...
Grzegorz Chrupala, Josef van Genabith
118
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ICASSP
2011
IEEE
14 years 6 months ago
Compressed learning of high-dimensional sparse functions
This paper presents a simple randomised algorithm for recovering high-dimensional sparse functions, i.e. functions f : [0, 1]d → R which depend effectively only on k out of d va...
Karin Schnass, Jan Vybíral
ADCM
2006
74views more  ADCM 2006»
15 years 3 months ago
Linearly constrained reconstruction of functions by kernels with applications to machine learning
This paper investigates the approximation of multivariate functions from data via linear combinations of translates of a positive definite kernel from a reproducing kernel Hilbert...
Robert Schaback, J. Werner