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KDD
2009
ACM
224views Data Mining» more  KDD 2009»
15 years 11 months ago
Issues in evaluation of stream learning algorithms
Learning from data streams is a research area of increasing importance. Nowadays, several stream learning algorithms have been developed. Most of them learn decision models that c...
João Gama, Raquel Sebastião, Pedro P...
SOCIALCOM
2010
15 years 4 months ago
Mining Human Location-Routines Using a Multi-Level Approach to Topic Modeling
In this work we address the problem of modeling varying time duration sequences for large-scale human routine discovery from cellphone sensor data using a multi-level approach to p...
Katayoun Farrahi, Daniel Gatica-Perez
AAAI
2008
15 years 9 months ago
Latent Tree Models and Approximate Inference in Bayesian Networks
We propose a novel method for approximate inference in Bayesian networks (BNs). The idea is to sample data from a BN, learn a latent tree model (LTM) from the data offline, and wh...
Yi Wang, Nevin Lianwen Zhang, Tao Chen
JMLR
2006
118views more  JMLR 2006»
15 years 6 months ago
Learning Factor Graphs in Polynomial Time and Sample Complexity
We study the computational and sample complexity of parameter and structure learning in graphical models. Our main result shows that the class of factor graphs with bounded degree...
Pieter Abbeel, Daphne Koller, Andrew Y. Ng
DIS
2009
Springer
16 years 1 months ago
An Iterative Learning Algorithm for Within-Network Regression in the Transductive Setting
Within-network regression addresses the task of regression in partially labeled networked data where labels are sparse and continuous. Data for inference consist of entities associ...
Annalisa Appice, Michelangelo Ceci, Donato Malerba