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» On the Complexity of Function Learning
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VTS
2006
IEEE
108views Hardware» more  VTS 2006»
15 years 6 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
87
Voted
GECCO
2005
Springer
136views Optimization» more  GECCO 2005»
15 years 6 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
83
Voted
ACL
2006
15 years 2 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
ICASSP
2011
IEEE
14 years 4 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
96
Voted
ADCM
2006
74views more  ADCM 2006»
15 years 20 days 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