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» GraphLab: A New Framework for Parallel Machine Learning
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COLT
2000
Springer
13 years 12 months ago
Average-Case Complexity of Learning Polynomials
The present paper deals with the averagecase complexity of various algorithms for learning univariate polynomials. For this purpose an appropriate framework is introduced. Based o...
Frank Stephan, Thomas Zeugmann
ICML
2007
IEEE
14 years 8 months ago
Learning random walks to rank nodes in graphs
Ranking nodes in graphs is of much recent interest. Edges, via the graph Laplacian, are used to encourage local smoothness of node scores in SVM-like formulations with generalizat...
Alekh Agarwal, Soumen Chakrabarti
ICIP
2006
IEEE
14 years 9 months ago
Knowledge-Based Supervised Learning Methods in a Classical Problem of Video Object Tracking
In this paper we present a new scheme for detection and tracking of specific objects in a knowledge-based framework. The scheme uses a supervised learning method: Support Vector M...
Lionel Carminati, Jenny Benois-Pineau, Christian J...
ICMLA
2009
13 years 5 months ago
Learning Deep Neural Networks for High Dimensional Output Problems
State-of-the-art pattern recognition methods have difficulty dealing with problems where the dimension of the output space is large. In this article, we propose a new framework ba...
Benjamin Labbé, Romain Hérault, Cl&e...
CONCUR
2010
Springer
13 years 8 months ago
Learning I/O Automata
Links are established between three widely used modeling frameworks for reactive systems: the ioco theory of Tretmans, the interface automata of De Alfaro and Henzinger, and Mealy ...
Fides Aarts, Frits W. Vaandrager