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ICML
2007
IEEE
16 years 7 months ago
Learning distance function by coding similarity
We consider the problem of learning a similarity function from a set of positive equivalence constraints, i.e. 'similar' point pairs. We define the similarity in informa...
Aharon Bar-Hillel, Daphna Weinshall
KDD
2005
ACM
109views Data Mining» more  KDD 2005»
16 years 7 months ago
Formulating distance functions via the kernel trick
Tasks of data mining and information retrieval depend on a good distance function for measuring similarity between data instances. The most effective distance function must be for...
Gang Wu, Edward Y. Chang, Navneet Panda
CEC
2005
IEEE
16 years 4 days ago
XCS with computed prediction for the learning of Boolean functions
Computed prediction represents a major shift in learning classifier system research. XCS with computed prediction, based on linear approximators, has been applied so far to functi...
Pier Luca Lanzi, Daniele Loiacono, Stewart W. Wils...
GECCO
2005
Springer
195views Optimization» more  GECCO 2005»
16 years 1 days ago
Evolutionary strategies for multi-scale radial basis function kernels in support vector machines
In support vector machines (SVM), the kernel functions which compute dot product in feature space significantly affect the performance of classifiers. Each kernel function is suit...
Tanasanee Phienthrakul, Boonserm Kijsirikul
FOCM
2011
117views more  FOCM 2011»
15 years 1 months ago
Accuracy and Stability of Computing High-order Derivatives of Analytic Functions by Cauchy Integrals
Abstract High-order derivatives of analytic functions are expressible as Cauchy integrals over circular contours, which can very effectively be approximated, e.g., by trapezoidal s...
Folkmar Bornemann