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» Structural Machine Learning with Galois Lattice and Graphs
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ICML
2007
IEEE
15 years 10 months ago
Scalable modeling of real graphs using Kronecker multiplication
Given a large, real graph, how can we generate a synthetic graph that matches its properties, i.e., it has similar degree distribution, similar (small) diameter, similar spectrum,...
Jure Leskovec, Christos Faloutsos
ML
2008
ACM
146views Machine Learning» more  ML 2008»
14 years 9 months ago
Improving maximum margin matrix factorization
Abstract. Collaborative filtering is a popular method for personalizing product recommendations. Maximum Margin Matrix Factorization (MMMF) has been proposed as one successful lear...
Markus Weimer, Alexandros Karatzoglou, Alex J. Smo...
SAC
2005
ACM
15 years 3 months ago
Discovering parametric clusters in social small-world graphs
We present a strategy for analyzing large, social small-world graphs, such as those formed by human networks. Our approach brings together ideas from a number of different resear...
Jonathan McPherson, Kwan-Liu Ma, Michael Ogawa
ICALT
2006
IEEE
15 years 3 months ago
A Shortest Learning Path Selection Algorithm in E-learning
Generally speaking, in the e-learning systems, a course is modeled as a graph, where each node represents a knowledge node (KU) and two nodes are connected to form a semantic netw...
Chengling Zhao, Liyong Wan
63
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ICALT
2003
IEEE
15 years 2 months ago
Visualization of the Learning Process Using Concept Mapping
Visualization of the learning processes is a powerful way to help students to understand their curricula and the structure behind them. CME2 is a prototype software of this favour...
Jussi A. Nuutinen, Erkki Sutinen