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» Bases for parametrized iterativity
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106
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ICML
2010
IEEE
15 years 2 months ago
Nonparametric Information Theoretic Clustering Algorithm
In this paper we propose a novel clustering algorithm based on maximizing the mutual information between data points and clusters. Unlike previous methods, we neither assume the d...
Lev Faivishevsky, Jacob Goldberger
CORR
2008
Springer
123views Education» more  CORR 2008»
15 years 1 months ago
Inference of Flow Statistics via Packet Sampling in the Internet
We show in this note that by deterministic packet sampling, the tail of the distribution of the original flow size can be obtained by rescaling that of the sampled flow size. To re...
Yousra Chabchoub, Christine Fricker, Fabrice Guill...
124
Voted
CG
2004
Springer
15 years 1 months ago
A barcode shape descriptor for curve point cloud data
In this paper, we present a complete computational pipeline for extracting a compact shape descriptor for curve point cloud data. Our shape descriptor, called a barcode, is based ...
Anne D. Collins, Afra Zomorodian, Gunnar Carlsson,...
CORR
2002
Springer
106views Education» more  CORR 2002»
15 years 1 months ago
On model selection and the disability of neural networks to decompose tasks
A neural network with fixed topology can be regarded as a parametrization of functions, which decides on the correlations between functional variations when parameters are adapted...
Marc Toussaint
INFORMATICALT
2002
136views more  INFORMATICALT 2002»
15 years 1 months ago
Comparison of Poisson Mixture Models for Count Data Clusterization
Abstract. Five methods for count data clusterization based on Poisson mixture models are described. Two of them are parametric, the others are semi-parametric. The methods emlploy ...
Jurgis Susinskas, Marijus Radavicius